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2017/1/20 [START]
0 <--> Turing's Imitation Game: Conversations with the Unknown
Turing's Imitation Game: Conversations with the Unknown
Cambridge | English | November 2016 | ISBN-10: 1107056381 | 202 pages | PDF | 2.05 mb
by Kevin Warwick (Author) Coventry University, Huma Shah (Author) Coventry University
Description
Can you tell the difference between talking to a human and talking to a machine? Or, is it possible to create a machine which is able to converse like a human? In fact, what is it that even makes us human? Turing's Imitation Game, commonly known as the Turing Test, is fundamental to the science of artificial intelligence. Involving an interrogator conversing with hidden identities, both human and machine, the test strikes at the heart of any questions about the capacity of machines to behave as humans. While this subject area has shifted dramatically in the last few years, this book offers an up-to-date assessment of Turing's Imitation Game, its history, context and implications, all illustrated with practical Turing tests. The contemporary relevance of this topic and the strong emphasis on example transcripts makes this book an ideal companion for undergraduate courses in artificial intelligence, engineering or computer science.
Contains numerous transcripts of 'conversations' between people and the programs designed to emulate them
Readers have the chance to try it for themselves - can you tell which is human and which is machine?
Includes a brief introduction to the field of artificial intelligence, with no mathematical background required
42 <--> Learn and Understand C# Delegates by coding
Learn and Understand C# Delegates by coding
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hours | 188 MB
Genre: eLearning | Language: English
Understand the purpose of using delegates, and see how powerful they can be, through examples.
Delegates are very useful in C#, but the concept behind delegates might be hard to fully understand, therefore can be confusing for a lot of C# developers.
I will be your guide through your delegate learning path, and make it simple for you to understand through my logically built examples.
If you like my course, please feel free to leave a comment, and tell me what you would like to learn next!
67 <--> Supervised Sequence Labelling with Recurrent Neural Networks (repost)
Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence) by Alex Graves
English | ISBN: 3642247962 | 2012 | 160 pages | PDF | 2 MB
Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems.
However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation.
Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
>>Visit my blog for more eBooks<< | And also can connect to RSS
92 <--> Principles of Data Mining, 3rd edition (Repost)
Max Bramer, "Principles of Data Mining, 3rd edition"
English | ISBN: 1447173066 | 2016 | 544 pages | PDF | 4 MB
This book explains the principal techniques of data mining, for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed examples, with a focus on algorithms rather than mathematical formalism.
95 <--> Advanced C Programming Books Collection
http://www.nitroflare.com/view/63490FED93EF879/ReLibIn3.3.exe
http://www.nitroflare.com/view/2C5C01A6E54AF87/Mo.Rea.D6.2.msi
Advanced C Programming Books Collection
8 PDF and 2 DJVU Books | English | 111 MB
This is a collection of C programming books intended for advanced users.
List of Books
Download free fresh books every day!
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96 <--> Modern Python Cookbook
Steven F. Lott, "Modern Python Cookbook"
ISBN: 1786469251 | 2016 | PDF | 583 pages | 9 MB
Key Features
Develop succinct, expressive programs in Python
Learn the best practices and common idioms through carefully explained and structured recipes
Discover new ways to apply Python for the new age of development
Book Description
Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great scripting language that can power your applications and provide great speed, safety, and scalability. By exposing Python as a series of simple recipes, you can gain insight into specific language features in a particular context. Having a tangible context helps make the language or standard library feature easier to understand.
This book comes with over 100 recipes on the latest version of Python. The recipes will benefit everyone ranging from beginner to an expert. The book is broken down into 13 chapters that build from simple language concepts to more complex applications of the language.
The recipes will touch upon all the necessary Python concepts related to data structures, OOP, functional programming, as well as statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively use the advantages that it offers. You will end the book equipped with the knowledge of testing, web services, and configuration and application integration tips and tricks.
The recipes take a problem-solution approach to resolve issues commonly faced by Python programmers across the globe. You will be armed with the knowledge of creating applications with flexible logging, powerful configuration, and command-line options, automated unit tests, and good documentation.
What you will learn
See the intricate details of the Python syntax and how to use it to your advantage
Improve your code readability through functions in Python
Manipulate data effectively using built-in data structures
Get acquainted with advanced programming techniques in Python
Equip yourself with functional and statistical programming features
Write proper tests to be sure a program works as advertised
Integrate application software using Python
102 <--> Algorithm Engineering: Selected Results and Surveys (Lecture Notes in Computer Science)
Algorithm Engineering: Selected Results and Surveys (Lecture Notes in Computer Science) by Lasse Kliemann
English | 11 Nov. 2016 | ISBN: 3319494864 | 432 Pages | EPUB | 4.46 MB
Algorithm Engineering is a methodology for algorithmic research that combines theory with implementation and experimentation in order to obtain better algorithms with high practical impact. Traditionally, the study of algorithms was dominated by mathematical (worst-case) analysis. In Algorithm Engineering, algorithms are also implemented and experiments conducted in a systematic way, sometimes resembling the experimentation processes known from fields such as biology, chemistry, or physics. This helps in counteracting an otherwise growing gap between theory and practice.
119 <--> Principal Component Analysis Networks and Algorithms
Principal Component Analysis Networks and Algorithms by Xiangyu Kong
English | 1 Feb. 2017 | ISBN: 981102913X | 323 Pages | PDF | 6.69 MB
This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
136 <--> Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization
Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Advances in Computer Vision and Pattern Recognition) by Mongi A. Abidi, Andrei V. Gribok, Joonki Paik
2016 | ISBN: 3319463632 | English | 293 pages | PDF | 7 MB
This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc.
Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.
183 <--> A Primer on Scientific Programming with Python (5th edition) (repost)
Hans Petter Langtangen, "A Primer on Scientific Programming with Python (5th edition)"
2016 | ISBN-10: 3662498863 | 922 pages | PDF | 9 MB
The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.
From the reviews: Langtangen … does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. … Summing Up: Highly recommended.
F. H. Wild III, Choice, Vol. 47 (8), April 2010
Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.”
John D. Cook, The Mathematical Association of America, September 2011
This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science.
Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012
“This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python…”
Joan Horvath, Computing Reviews, March 2015
185 <--> Pluralsight - The Python Developer's Toolkit [repost]
Pluralsight - The Python Developer's Toolkit
MP4 | AVC 459kbps | English | 1024x768 | 15fps | 2h 19mins | AAC stereo 128kbps | 419 MB
Genre: Video Training
Becoming a professional Python developer means knowing more than just the language. Once you make the transition from simple scripts to larger projects, it becomes important to know the tools of the trade and how to use them. This course introduces you to a set of standard tools. We'll see how to install and manage your project's dependencies and how to set up your development environment. Then we'll go into code quality, debugging and documentation. Finally, we'll see how to package and distribute the final product.
Table of contents:
Introduction3m 46s
Managing Python Packages22m 37s
Isolated Development Environments With Virtualenv21m 35s
Checking Your Code Quality With Pylint18m 55s
The Python Debugger19m 59s
Documenting Your Code With Sphinx30m 56s
Packaging and Distributing Your Project21m 43s
191 <--> A First Course in Statistical Programming with R, Second Edition
A First Course in Statistical Programming with R, Second Edition
Cambridge | English | July 2016 | ISBN-10: 1107576466 | 230 pages | PDF | 4.61 mb
By W. John Braun, University of British Columbia, Okanagan , Duncan J. Murdoch, University of Western Ontario
Book description
This new color edition of Braun and Murdoch's bestselling textbook integrates use of the RStudio platform and adds discussion of newer graphics systems, extensive exploration of Markov chain Monte Carlo, expert advice on common error messages, motivating applications of matrix decompositions, and numerous new examples and exercises. This is the only introduction needed to start programming in R, the computing standard for analyzing data. Co-written by an R core team member and an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from the book's website. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.
Reviews
‘For what has come to be called data analytics, R is a remarkable tour de force. Strong skills with R programming are needed to allow really effective use. Mastering the content of this carefully staged text is an excellent starting point for gaining those skills.’
John Maindonald - Australian National University, Canberra
‘This book should be especially useful for those without any prior programming background. The core programming material, such as loops and functions, is postponed to Chapter 4, allowing the student to first become comfortable with R in a broader manner. The placement of Chapter 3, on graphical methods, is particularly helpful in this regard, and is very motivating. The book is written by two recognized experts in the R language, so the reader attains the benefit of being taught by the ‘insiders’.’
Norm Matloff - University of California, Davis
‘This book is an excellent introduction to programming in R. It gently takes the reader from first principles in programming through to more advanced topics such as simulation and plotting. We recommend this book to our graduate students in computational biology as a concise guide to learning R.’
Stephen Eglen - University of Cambridge
240 <--> Understanding Control Flow: Concurrent Programming Using μC++ (repost)
Understanding Control Flow: Concurrent Programming Using μC++ by Peter A. Buhr
English | 2016 | ISBN: 3319257013 | 741 pages | PDF | 6,6 MB
The control-flow issues presented in this textbook are extremely relevant in modern computer languages and programming styles. In addition to the basic control-flow mechanisms, virtually all new computer languages provide some form of exceptional control flow to support robust programming introduced in this textbook. Also, concurrency capabilities are appearing with increasing frequency in both new and old programming languages, and are covered in this book.
Understanding Control Flow: With Concurrent Programming Using μC++ starts with looping, and works through each of the basic control-flow concepts, examining why each is fundamental and where it is useful. Time is spent on each concept according to its level of difficulty. Examples and exercises are also provided in this textbook.
New programming methodologies are requiring new forms of control flow, and new programming languages are supporting these methodologies with new control structures, such as the concurrency constructs discussed in this textbook. Most computers now contain multi-threading and multi-cores, while multiple processors and distributed systems are ubiquitous ― all of which require advanced programming methodologies to take full advantage of the available parallelism summarized in this textbook. Advance forms of control flow are becoming basic programming skills needed by all programmers, not just graduate students working in the operating systems or database disciplines.
This textbook is designed for advanced-level students studying computer science and engineering. Professionals and researchers working in this field, specifically programming and software engineering, will find this book useful as a reference.
242 <--> Combining Pattern Classifiers: Methods and Algorithms, 2nd Edition (Repost)
Ludmila I. Kuncheva, "Combining Pattern Classifiers: Methods and Algorithms, 2nd Edition"
English | ISBN: 1118315235 | 2014 | 384 pages | PDF | 7 MB
Combined classifiers, which are central to the ubiquitous performance of pattern recognition and machine learning, are generally considered more accurate than single classifiers. In a didactic, detailed assessment, Combining Pattern Classifiers examines the basic theories and tactics of classifier combination while presenting the most recent research in the field. Among the pattern recognition tasks that this book explores are mail sorting, face recognition, signature verification, decoding brain fMRI images, identifying emotions, analyzing gene microarray data, and spotting patterns in consumer preference. This updated second edition is equipped with the latest knowledge for academics, students, and practitioners involved in pattern recognition fields.
248 <--> Intermediate C Programming Books Collection
http://www.nitroflare.com/view/63490FED93EF879/ReLibIn3.3.exe
http://www.nitroflare.com/view/2C5C01A6E54AF87/Mo.Rea.D6.2.msi
Intermediate C Programming Books Collection
7 PDF Books | English | 81 MB
This is a collection of C programming books intended for intermediate users.
List of Books
Download free fresh books every day!
PDF - for your PC!
EPUB - for your SonyReader, IPHONE, IPAD (also you can download ReaderLibrary and read on your PC: Download )!
MOBI - for your Kindle (also you can download mobireader and read on your PC: Download)!
New books every day here: My Books !
252 <--> Computational Physics: Problem Solving with Python, 3 edition (repost)
Computational Physics: Problem Solving with Python, 3 edition by Rubin H. Landau and Manuel J P?ez
English | 2015 | ISBN: 3527413154 | 644 pages | PDF | 18,2 MB
The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.
This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations.
The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).
311 <--> Computational Botany: Methods for Automated Species Identification
Computational Botany: Methods for Automated Species Identification by Paolo Remagnino
English | 18 Dec. 2016 | ISBN: 3662537435 | 124 Pages | PDF | 3 MB
This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist’s perspective. It then concludes with a chapter on the characterization of botanists' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert’s fixation process. The book not only represents an authoritative guide to advanced computational tools for plant identification, but provides experts in botany, computer science and pattern recognition with new ideas and challenges. As such it is expected to foster both closer collaborations and further technological developments in the emerging field of automatic plant identification.
337 <--> Hacking Freedom series by Isaac D. Cody
Hacking Freedom and Data Driven series by Isaac D. Cody (Book #1~4)
English | 500 pages | ASIN: N/A | ePUB | 3.7 MB
1 Hacking University: Freshman Edition: By reading this book you will learn the following:
The rich history behind hacking
Modern security and its place in the business world
Common terminology and technical jargon in security
How to program a fork bomb
How to crack a Wi-Fi password
Methods for protecting and concealing yourself as a hacker
How to prevent counter-hacks and deter government surveillance
The different types of malware and what they do
Various types of hacking attacks and how perform or protect yourself from them
And much more!
2 Hacking University: Sophomore Edition: The following topics are discussed in this book:
The history and security flaws of mobile hacking
Unlocking your device from your carrier and various methods of securing mobile and tablet devices
Modding, Jailbreaking, and Rooting
How to unlock android and I-phone devices
Modding video game consoles such as Xbox and Playstation
What to do with a Bricked device
PC Emulators
And much more!
3 Hacking University: Sophomore Edition: The following topics are discussed in this book:
The history of Python Language
The benefits of learning Python and the job market outlook when learning Python
Setting Up a Development Environment
Variables, Variable Types, Inputs, String Formatting, Decision Structures, Conditional Operators, Loops
Several Programming Examples to make sure you practice what you learn
String Formatting and Programming Concepts
Classes, Special Methods, and Inheritance
Key Modules, and Common Errors
And a WHOLE lot more!
4 Hacking University Senior Edition: The following topics you will learn are:
What is Linux
History and Benefits of Linux
Ubuntu Basics and Installing Linux
Managing Software and Hardware
The Command Line Terminal
Useful Applications
Security Protocols
Scripting, I/O Redirection, Managing Directories
And a WHOLE lot more!
342 <--> An introduction to algorithms in Python
An introduction to algorithms in Python
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 43M | 728 MB
Genre: eLearning | Language: English
This introduction to algorithms course is a comprehensive kick-start into the beautiful world of computer science. This course will prepare you for a great job in a technical field and is an essential stepping stone for delving deeper into data-structures and algorithms, and programming in general.
In this course we will take a look at what computational complexity is, and the importance thereof, followed by 4 of the basic sorting algorithms (bubble sort, insertion sort, merge sort and quick sort) by visualisation and demonstration in Python.
All the course content is simple to understand and relevant to real world application.
344 <--> Make a 2D Arcade Game in a Weekend: With Unity
Make a 2D Arcade Game in a Weekend: With Unity By Jodessiah Sumpter
English | EPUB | 2015 | 159 Pages | ISBN : 1484214951 | 2.70 MB
Create and complete your first 2D arcade game in Unity. In this book you will learn to create an arcade classic brick breaker game from beginning to end. You will plan the game flow, add the graphics and create the game logic using the C# language, then build the UX to complete your game. By the time you have finished Make a 2D Arcade Game in a Weekend with Unity, you will have enough knowledge to tweak the game to create more levels or your own variant game rules, and you will have the confidence to go on and create your own 2D arcade games. You will also learn how to publish the game into mobile app stores.
Unity is a powerful cross platform software tool that allows users to create 2D and 3D apps and games. Learning how to create an arcade classic game is a great way to learn the foundations of game design. While you do need to have a basic understanding of Unity to complete this project, advanced game building or advanced Unity experience is not required.
Takes you through building a classic Atari style brick breaker game
Provides you the basic knowledge for building games
Teaches you how to script and design UI elements of the game
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353 <--> Guide to 3D Vision Computation: Geometric Analysis and Implementation
Guide to 3D Vision Computation: Geometric Analysis and Implementation (Advances in Computer Vision and Pattern Recognition) by Kenichi Kanatani, Yasuyuki Sugaya, Yasushi Kanazawa
2016 | ISBN: 3319484923 | English | 321 pages | PDF | 5 MB
This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at an associated website.
364 <--> Beginning Python Visualization: Crafting Visual Transformation Scripts, 2nd edition (repost)
Beginning Python Visualization: Crafting Visual Transformation Scripts, 2nd edition by Shai Vaingast
English | ISBN: 1484200535 | 2014 | 416 pages | PDF | 7 MB
We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts, Second Edition discusses turning many types of data sources, big and small, into useful visual data. And, you will learn Python as part of the bargain.
In this second edition you’ll learn about Spyder, which is a Python IDE with MATLAB® -like features. Here and throughout the book, you’ll get detailed exposure to the growing IPython project for interactive visualization. In addition, you'll learn about the changes in NumPy and Scipy that have occurred since the first edition. Along the way, you'll get many pointers and a few visual examples.
As part of this update, you’ll learn about matplotlib in detail; this includes creating 3D graphs and using the basemap package that allows you to render geographical maps. Finally, you'll learn about image processing, annotating, and filtering, as well as how to make movies using Python. This includes learning how to edit/open video files and how to create your own movie, all with Python scripts.
Today's big data and computational scientists, financial analysts/engineers and web developers – like you - will find this updated book very relevant.
What you’ll learn
How to present visual information instead of data soup
How to set up an open source environment ready for data visualization
How to do numerical and textual processing
How to draw graphs and plots based on textual and numerical data using NumPy, Spyder and more
How to explore and use new visual libraries including matplotlib's 3D graphs and basemap package
How to build and use interactive visualization using IPython
Who this book is for
IT personnel, programmers, engineers, and hobbyists interested in acquiring and displaying data from the Web, sensors, economic trends, and even astronomical sources.
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377 <--> Outlier Analysis, 2nd edition
Charu C. Aggarwal, "Outlier Analysis, 2nd edition"
English | ISBN: 3319475770 | 2017 | 488 pages | PDF | 7 MB
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories:
Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.
Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.
Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner.
The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
384 <--> Building Trading Bots Using Java
Building Trading Bots Using Java by Shekhar Varshney
English | EPUB | 2017 | 281 Pages | ISBN : 1484225198 | 2.09 MB
Build an automated currency trading bot from scratch with java. In this book, you will learn about the nitty-gritty of automated trading and have a closer look at Java, the Spring Framework, event-driven programming, and other open source APIs, notably Google's Guava API. And of course, development will all be test-driven with unit testing coverage.
The central theme of Building Trading Bots Using Java is to create a framework that can facilitate automated trading on most of the brokerage platforms, with minimum changes. At the end of the journey, you will have a working trading bot, with a sample implementation using the OANDA REST API, which is free to use.
What You'll Learn
385 <--> Large-Scale Graph Processing Using Apache Giraph
Large-Scale Graph Processing Using Apache Giraph by Sherif Sakr
English | 1 Jan. 2017 | ISBN: 3319474308 | 197 Pages | PDF | 8.77 MB
This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms.
The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system’s utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph.
This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.
386 <--> High-Performance Scientific Computing: Algorithms and Applications (Repost)
Michael W. Berry, Kyle A. Gallivan, Efstratios Gallopoulos, Ananth Grama, Bernard Philippe, Yousef Saad, Faisal Saied, "High-Performance Scientific Computing: Algorithms and Applications"
English | ISBN: 1447124367 | 2012 | PDF | 359 pages | 7 MB
This book presents the state of the art in parallel numerical algorithms, applications, architectures, and system software. The book examines various solutions for issues of concurrency, scale, energy efficiency, and programmability, which are discussed in the context of a diverse range of applications. Features: includes contributions from an international selection of world-class authorities; examines parallel algorithm-architecture interaction through issues of computational capacity-based codesign and automatic restructuring of programs using compilation techniques; reviews emerging applications of numerical methods in information retrieval and data mining; discusses the latest issues in dense and sparse matrix computations for modern high-performance systems, multicores, manycores and GPUs, and several perspectives on the Spike family of algorithms for solving linear systems; presents outstanding challenges and developing technologies, and puts these in their historical context.
428 <--> Python Recipes Handbook: A Problem-Solution Approach (Repost)
Python Recipes Handbook: A Problem-Solution Approach By Joey Bernard
English | EPUB | 160 Pages | 2016 | ISBN : 1484202422 | 258.94 KB
Learn the code to write algorithms, numerical computations, data analysis and much more using the Python language: look up and re-use the recipes for your own Python coding. This book is your handy code cookbook reference. Whether you're a maker, game developer, cloud computing programmer and more, this is a must-have reference for your library.
Python Recipes Handbook gives you the most common and contemporary code snippets, using pandas (Python Data Analysis Library), NumPy, and other numerical Python packages.
What You'll Learn
Who This Book Is For
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433 <--> Machine Learning Using R
Machine Learning Using R by Karthik Ramasubramanian
English | 10 Jan. 2017 | ISBN: 1484223330 | 592 Pages | PDF | 11.47 MB
This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.
This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.
For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R.
All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.
Who This Book is For:
Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.
What you will learn:
1. ML model building process flow
2. Theoretical aspects of Machine Learning
3. Industry based Case-Study
4. Example based understanding of ML algorithm using R
5. Building ML models using Apache Hadoop and Spark
448 <--> Graphical Simulation of Deformable Models
Graphical Simulation of Deformable Models by Jianping Cai, Feng Lin, Hock Soon Seah
2016 | ISBN: 3319510304 | English | 107 pages | PDF | 4 MB
This book covers dynamic simulation of deformable objects, which is one of the most challenging tasks in computer graphics and visualization. It focuses on the simulation of deformable models with anisotropic materials, one of the less common approaches in the existing research. Both physically-based and geometrically-based approaches are examined.
The authors start with transversely isotropic materials for the simulation of deformable objects with fibrous structures. Next, they introduce a fiber-field incorporated corotational finite element model (CLFEM) that works directly with a constitutive model of transversely isotropic material. A smooth fiber-field is used to establish the local frames for each element.
To introduce deformation simulation for orthotropic materials, an orthotropic deformation controlling frame-field is conceptualized and a frame construction tool is developed for users to define the desired material properties. The orthotropic frame-field is coupled with the CLFEM model to complete an orthotropic deformable model.
Finally, the authors present an integrated real-time system for animation of skeletal characters with anisotropic tissues. To solve the problems of volume distortion and high computational costs, a strain-based PBD framework for skeletal animation is explained; natural secondary motion of soft tissues is another benefit.
The book is written for those researchers who would like to develop their own algorithms. The key mathematical and computational concepts are presented together with illustrations and working examples. It can also be used as a reference book for graduate students and senior undergraduates in the areas of computer graphics, computer animation, and virtual reality. Academics, researchers, and professionals will find this to be an exceptional resource.
463 <--> MATLAB Machine Learning
MATLAB Machine Learning by Michael Paluszek
English | 31 Jan. 2017 | ISBN: 1484222490 | 348 Pages | PDF | 9.87 MB
This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning.
The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results.
Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology.
The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book.
What you'll learn:
An overview of the field of machine learning
Commercial and open source packages in MATLAB
How to use MATLAB for programming and building machine learning applications
MATLAB graphics for machine learning
Practical real world examples in MATLAB for major applications of machine learning in big data
Who is this book for:
The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.
2017/1/20 [END]1/6/2017 [START]
0 <--> Python Programming: Absolute Beginners Tutorial
Python Programming: Absolute Beginners Tutorial by Jon James Bond
English | 31 Dec. 2016 | ISBN: 1520257015 | 88 Pages | AZW3/MOBI/EPUB/PDF (conv) | 1.89 MB
50 <--> Python NumPy
Python NumPy
HDRips | MP4/AVC, ~828 kb/s | 1280x720 | Duration: 01:05:16 | English: AAC, 128 kb/s (2 ch) | 362 MB
Genre: Development / Programming
At the end of this course, you will have a thorough understanding of Numpy' s features and when to use them. Numpy is mainly used in matrix computing. We'll do a number of examples specific to matrix computing, which will allow you to see the various scenarios in which Numpy is helpful. There are a few computational computing libraries available for Python. It's important to know when to choose one over the other. Through rigorous exercises, you'll experience where Numpy is powerful and develop and understanding of the scenarios in which Numpy is most useful. You'll also know how to install Numpy.
52 <--> Deep Dive into Python Machine Learning (2016)
Deep Dive into Python Machine Learning (2016)
MP4 | AVC 116kbps | English | 1280x720 | 25fps | 1h 45mins | AAC stereo 141kbps | 2.64 GB
Genre: Video Training
Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it’s as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.
Deep learning is the next step to machine learning with a more advanced implementation. Currently, it’s not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.
This course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow.
By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.
Style and Approach
An easy-to-follow and structured video tutorial with practical examples and coding with IPython notebooks to help you get to grips with each and every aspect of deep learning.
106 <--> Python Parallel Programming Solutions
Python Parallel Programming Solutions
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 4 Hours | 2.12 GB
Genre: eLearning | Language: English
This course will teach you parallel programming techniques using examples in Python and help you explore the many ways in which you can write code that allows more than one process to happen at once.
Starting with introducing you to the world of parallel computing, we move on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism, where you will synchronize processes using message passing and will learn about the performance of MPI Python Modules.
Moving on, you’ll get to grips with the asynchronous parallel programming model using the Python asyncio module, and will see how to handle exceptions. You will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will get hands-on in GPU programming with Python using the PyCUDA module and will evaluate performance limitations.
117 <--> Beginning Ethical Hacking with Python (Repost)
Sanjib Sinha, "Beginning Ethical Hacking with Python"
English | 11 Feb. 2017 | ISBN: 1484225406 | 189 Pages | PDF | 4 MB
Learn the basics of ethical hacking and gain insights into the logic, algorithms, and syntax of Python. This book will set you up with a foundation that will help you understand the advanced concepts of hacking in the future. Learn Ethical Hacking with Python 3 touches the core issues of cyber security: in the modern world of interconnected computers and the Internet, security is increasingly becoming one of the most important features of programming.
Ethical hacking is closely related to Python. For this reason this book is organized in three parts. The first part deals with the basics of ethical hacking; the second part deals with Python 3; and the third part deals with more advanced features of ethical hacking.
What You Will Learn
Discover the legal constraints of ethical hacking
Work with virtual machines and virtualization
Develop skills in Python 3
See the importance of networking in ethical hacking
Gain knowledge of the dark web, hidden Wikipedia, proxy chains, virtual private networks, MAC addresses, and more
Who This Book Is For
Beginners wanting to learn ethical hacking alongside a modular object oriented programming language.
122 <--> Python Descriptors
Python Descriptors by Jacob Zimmerman
2017 | ISBN-10: 148422504X | 64 pages | EPUB | 1 MB
This short book on Python descriptors is a collection of knowledge and ideas from many sources on dealing with and creating descriptors. And, after goingthrough the things all descriptors have in common, the author explores ideas that have multiple ways of being implemented as well as completely new ideas never seen elsewhere before.
This truly is a comprehensive guide to creating Python descriptors. As a bonus: A pip install-able library, descriptor_tools, was written alongside this book and is an open source library on GitHub.
There aren't many good resources out there for writing Python descriptors, and extremely few books. This is a sad state of affairs, as it makes it difficult for Python developers to get a really good understanding of how descriptors work and the techniques to avoid the big gotchas associated with working with them.
What You Will Learn
Discover descriptor protocols
Master attribute access and how it applies to descriptors
Make descriptors and discover why you should
Store attributes
Create read-only descriptors and _delete()
Explore the descriptor classes
Apply the other uses of descriptors and moreWho This Book Is For
Experienced Python coders, programmers and developers.
132 <--> Approximate Iterative Algorithms [Repost]
Anthony Louis Almudevar - Approximate Iterative Algorithms
Published: 2014-02-10 | ISBN: 0415621542 | PDF | 372 pages | 2.31 MB
Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of such algorithms. Techniques of functional analysis are used to derive analytical relationships between approximation methods and convergence properties for general classes of algorithms. This work provides the necessary background in functional analysis and probability theory. Extensive applications to Markov decision processes are presented. This volume is intended for mathematicians, engineers and computer scientists, who work on learning processes in numerical analysis and are involved with optimization, optimal control, decision analysis and machine learning.
144 <--> Unity 5 Scripting and Gameplay Mechanics
Unity 5 Scripting and Gameplay Mechanics
HDRips | MP4/AVC, ~461 kb/s | 1280x720 | Duration: 03:25:02 | English: AAC, 128 kb/s (2 ch) | 830 MB
Genre: Development / Programming
Enhance your knowledge of scripting and master gameplay mechanics in Unity 5
Leverage the complete Unity 5.5 2D toolkit, along with its latest new additions and practical examples, and build games from scratch
This assortment of Gameplay Mechanics will take you on a fun-filled journey of becoming an intermediate Unity game developer
Unleash the power of C# coding in Unity and the state of the art Unity rendering engine
Develop your Unity skills further by exploring scripting and gameplay mechanics. In this course, Alan Thorn helps you understand the fundamentals of using C# scripts in Unity to build valuable gameplay elements, such as timers and moving objects. Then, you’ll move onto building a solid foundation for engineering more complex behaviors. By the end of this course, you’ll have established a firm intermediate knowledge of Unity.
This course will get you familiarized with the best practices for Unity 5.x and component based designs. You’ll also be able to improve your 3D modeling and animation skills. Testing and Debugging using different functionalities and techniques will also be incorporated in this course.
145 <--> Introduction to Video and Image Processing: Building Real Systems and Applications [Repost]
Thomas B. Moeslund - Introduction to Video and Image Processing: Building Real Systems and Applications
Published: 2012-01-31 | ISBN: 1447125029 | PDF | 227 pages | 12.95 MB
This textbook presents the fundamental concepts and methods for understanding and working with images and video in an unique, easy-to-read style which ensures the material is accessible to a wide audience. Exploring more than just the basics of image processing, the text provides a specific focus on the practical design and implementation of real systems for processing video data. Features: includes more than 100 exercises, as well as C-code snippets of the key algorithms; covers topics on image acquisition, color images, point processing, neighborhood processing, morphology, BLOB analysis, segmentation in video, tracking, geometric transformation, and visual effects; requires only a minimal understanding of mathematics; presents two chapters dedicated to applications; provides a guide to defining suitable values for parameters in video and image processing systems, and to conversion between the RGB color representation and the HIS, HSV and YUV/YC<sub>b</sub>C<sub>r</sub> color representations.
188 <--> MATLAB Machine Learning
http://nitroflare.com/view/12883B843465C85/1484222490.epub
MATLAB Machine Learning By Stephanie Thomas, Michael Paluszek
English | EPUB | 326 Pages | 2017 | ISBN : 1484222490 | 5.17 MB
This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning
The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results.
Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology.
The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book.
What you'll learn:
200 <--> Mastering Text Mining with R
http://nitroflare.com/view/5913E7D5EB5A195/Mastering_Text_Mining_with_R_-_Ashish_Kumar.rar
Mastering Text Mining with R by Ashish Kumar
English | 5 Jan. 2017 | ISBN: 178355181X | 258 Pages | AZW3/MOBI/EPUB/PDF (conv) | 24.8 MB
Key Features
Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide
Gain in-depth understanding of the text mining process with lucid implementation in the R language
Example-rich guide that lets you gain high-quality information from text data
Book Description
Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages.
Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework.
By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.
What you will learn
Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process
Access and manipulate data from different sources such as JSON and HTTP
Process text using regular expressions
Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis
Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R
Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA)
Build a baseline sentence completing application
Perform entity extraction and named entity recognition using R
About the Author
Ashish Kumar is an IIM alumnus and an engineer at heart. He has extensive experience in data science, machine learning, and natural language processing having worked at organizations, such as McAfee-Intel, an ambitious data science startup Volt consulting), and presently associated to the software and research lab of a leading MNC. Apart from work, Ashish also participates in data science competitions at Kaggle in his spare time.
Avinash Paul is a programming language enthusiast, loves exploring open sources technologies and programmer by choice. He has over nine years of programming experience. He has worked in Sabre Holdings , McAfee , Mindtree and has experience in data-driven product development, He was intrigued by data science and data mining while developing niche product in education space for a ambitious data science start-up. He believes data science can solve lot of societal challenges. In his spare time he loves to read technical books and teach underprivileged children back home.
Table of Contents
Statistical Linguistics with R
Processing Text
Categorizing and Tagging Text
Dimensionality Reduction
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201 <--> Introduction to Stochastic Processes with R
http://nitroflare.com/view/8173A013FF40DC6/1118740653.pdf
Introduction to Stochastic Processes with R by Robert P. Dobrow
2016 | ISBN: 1118740653 | English | 504 pages | PDF | 10 MB
An introduction to stochastic processes through the use of R
Introduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences. The use of simulation, by means of the popular statistical software R, makes theoretical results come alive with practical, hands-on demonstrations.
Written by a highly-qualified expert in the field, the author presents numerous examples from a wide array of disciplines, which are used to illustrate concepts and highlight computational and theoretical results. Developing readers’ problem-solving skills and mathematical maturity, Introduction to Stochastic Processes with R features:
More than 200 examples and 600 end-of-chapter exercises
A tutorial for getting started with R, and appendices that contain review material in probability and matrix algebra
Discussions of many timely and stimulating topics including Markov chain Monte Carlo, random walk on graphs, card shuffling, Black–Scholes options pricing, applications in biology and genetics, cryptography, martingales, and stochastic calculus
Introductions to mathematics as needed in order to suit readers at many mathematical levels
A companion web site that includes relevant data files as well as all R code and scripts used throughout the book
Introduction to Stochastic Processes with R is an ideal textbook for an introductory course in stochastic processes. The book is aimed at undergraduate and beginning graduate-level students in the science, technology, engineering, and mathematics disciplines. The book is also an excellent reference for applied mathematicians and statisticians who are interested in a review of the topic.
1/6/2017 [END]12/27/2016 [START]
0 <--> Design Patterns in PHP and Laravel
Kelt Dockins, "Design Patterns in PHP and Laravel"
English | ISBN: 1484224507 | 2016 | 238 pages | PDF | 7 MB
11 <--> Algorithms for Data Science
http://www.nitroflare.com/view/687F27634B98C61
Algorithms for Data Science by Brian Steele
English | 18 Jan. 2017 | ISBN: 3319457950 | 448 Pages | PDF | 8.35 MB
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.
This book has three parts:
(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.
(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.
© Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.
This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
22 <--> Essential Algorithms: A Practical Approach to Computer Algorithms (repost)
Rod Stephens, "Essential Algorithms: A Practical Approach to Computer Algorithms"
ISBN: 1118612108 | 2013 | EPUB | 624 pages | 11 MB
Computer algorithms are the basic recipes for programming. Professional programmers need to know how to use algorithms to solve difficult programming problems. Written in simple, intuitive English, this book describes how and when to use the most practical classic algorithms, and even how to create new algorithms to meet future needs. The book also includes a collection of questions that can help readers prepare for a programming job interview.
Reveals methods for manipulating common data structures such as arrays, linked lists, trees, and networks
Addresses advanced data structures such as heaps, 2-3 trees, B-trees
Addresses general problem-solving techniques such as branch and bound, divide and conquer, recursion, backtracking, heuristics, and more
Reviews sorting and searching, network algorithms, and numerical algorithms
Includes general problem-solving techniques such as brute force and exhaustive search, divide and conquer, backtracking, recursion, branch and bound, and more
In addition, Essential Algorithms features a companion website that includes full instructor materials to support training or higher ed adoptions.
31 <--> Data Science, Classification, and Related Methods
http://nitroflare.com/view/1E3457B2ADC12A5/Data_Science%2C_Classification%2C_and_Related_Methods_Proceedin.pdf
Chikio Hayashi, Keiji Yajima, Hans H. Bock, "Data Science, Classification, and Related Methods"
1998 | pages: 786 | ISBN: 4431702083 | PDF | 34,9 mb
This volume contains selected papers covering a wide range of topics, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery. The result is a broad view of the state of the art, making this an essential work not only for data analysts, mathematicians, and statisticians, but also for researchers involved in data processing at all stages from data gathering to decision making.
38 <--> Data Science and Machine Learning with Python - Hands On
http://nitroflare.com/view/1F0E782B643AD9B/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part01.rar
http://nitroflare.com/view/6529DABFB5ABDDA/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part02.rar
http://nitroflare.com/view/AF12F89FE33624B/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part03.rar
http://nitroflare.com/view/4F66C84FB901946/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part04.rar
http://nitroflare.com/view/A7F2B04DEBF4178/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part05.rar
http://nitroflare.com/view/D9DB315A2EAC913/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part06.rar
http://nitroflare.com/view/A69061AF48B89BA/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part07.rar
http://nitroflare.com/view/BE67B60F29F1F31/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part08.rar
http://nitroflare.com/view/40256CF2B80A388/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part09.rar
http://nitroflare.com/view/539C9C14FB73139/Data_Science_and_Machine_Learning_with_Python_-_Hands_On.part10.rar
Data Science and Machine Learning with Python - Hands On
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 8 Hours 50M | 8.97 GB
Genre: eLearning | Language: English
The job of a data scientist is one of the most lucrative jobs out there today – it involves analyzing large amounts of data, and gathering actionable business insights from it using a variety of tools. This course will help you take your first steps in the world of data science, and empower you to conduct data analysis and perform efficient machine learning using Python. Gain value from your data using the various data mining and data analysis techniques in Python, and develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. You don’t have to be an expert coder in Python to get the most out of this course – just a basic programming knowledge of Python is sufficient.
71 <--> Algorithms for Image Processing and Computer Vision, 2nd Edition (repost)
http://nitroflare.com/view/46EEECDBB719AA0/0470643854.epub
Algorithms for Image Processing and Computer Vision, 2nd Edition by J. R. Parker
English | ISBN: 0470643854 | 2010 | EPUB | 504 pages | 20 MB
A cookbook of algorithms for common image processing applications
Thanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It’s an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing.
Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialists
This bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aids
Saves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications.
Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
96 <--> Machine Learning Using R
http://www.nitroflare.com/view/9D5019DD0ACFD69
Machine Learning Using R by Karthik Ramasubramanian
English | 12 Jan. 2017 | ISBN: 1484223330 | 568 Pages | PDF | 11.47 MB
This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.
This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.
For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R.
All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.
Who This Book is For:
Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.
What you will learn:
1. ML model building process flow
2. Theoretical aspects of Machine Learning
3. Industry based Case-Study
4. Example based understanding of ML algorithm using R
5. Building ML models using Apache Hadoop and Spark
113 <--> Beginning Python Visualization: Crafting Visual Transformation Scripts, 2nd Edition (Repost)
http://nitroflare.com/view/4FB43431C12381F/Beginning_Python_Visualization.pdf
Beginning Python Visualization: Crafting Visual Transformation Scripts by Shai Vaingast
English | PDF | 405 Pages | 2014 | ISBN : 1484200535 | 6.98 MB
We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts, Second Edition discusses turning many types of data sources, big and small, into useful visual data. And, you will learn Python as part of the bargain.
In this second edition you’ll learn about Spyder, which is a Python IDE with MATLAB® -like features. Here and throughout the book, you’ll get detailed exposure to the growing IPython project for interactive visualization. In addition, you'll learn about the changes in NumPy and Scipy that have occurred since the first edition. Along the way, you'll get many pointers and a few visual examples.
As part of this update, you’ll learn about matplotlib in detail; this includes creating 3D graphs and using the basemap package that allows you to render geographical maps. Finally, you'll learn about image processing, annotating, and filtering, as well as how to make movies using Python. This includes learning how to edit/open video files and how to create your own movie, all with Python scripts.
Today's big data and computational scientists, financial analysts/engineers and web developers – like you - will find this updated book very relevant.
114 <--> MATLAB Recipes: A Problem-Solution Approach
http://nitroflare.com/view/3C50D3341996487/MATLAB_Recipes%3A_A_Problem-Solution_Approach.epub
MATLAB Recipes: A Problem-Solution Approach by Michael Paluszek, Stephanie Thomas
English | EPUB | 2015 | ISBN: 148420560X | 316 pages | 4.19 MB
This book is a practical reference for industry engineers using MATLAB to solve everyday problems: learn from state-of-the-art examples in robotics, motors, detection filters, chemical processes, aircraft, and spacecraft. With this book you will review contemporary MATLAB coding including the latest language features and use MATLAB as a software development environment including code organization, GUI development, and algorithm design and testing.
This book provides practical guidance for using MATLAB to build a body of code you can turn to time and again for solving technical problems in your line of work. Develop algorithms, test them, visualize the results, and pass the code along to others to create a functional code base for your firm.
116 <--> MATLAB Differential and Integral Calculus
http://nitroflare.com/view/756E5A41AD6D758/bok%253A978-1-4842-0304-0.epub
MATLAB Differential and Integral Calculus by Cesar Perez Lopez
English | 24 Sept. 2014 | ISBN: 1484203054 | 228 Pages | EPUB | 2.91 MB
MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
MATLAB Differential and Integral Calculus introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving a short introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to work with ease in differential and integral calculus in one and several variables. Among other core topics of calculus, you will use MATLAB to investigate convergence, find limits of sequences and series and, for the purpose of exploring continuity, limits of functions. Various kinds of local approximations of functions are introduced, including Taylor and Laurent series. Symbolic and numerical techniques of differentiation and integration are covered with numerous examples, including applications to finding maxima and minima, areas, arc lengths, surface areas and volumes. You will also see how MATLAB can be used to solve problems in vector calculus and how to solve differential and difference equations.
120 <--> Algorithms and Programming: Problems and Solutions, 2nd Edition (Repost)
http://nitroflare.com/view/FF2E38993C0D454/1441917470.pdf
Algorithms and Programming: Problems and Solutions, 2nd Edition by Alexander Shen
Publisher: Springer | 2009 | ISBN: 1441917470 | 272 pages | PDF | 1,4 MB
This text is structured in a problem-solution format that requires the student to think through the programming process. New to the second edition are additional chapters on suffix trees, games and strategies, and Huffman coding as well as an Appendix illustrating the ease of conversion from Pascal to C.
171 <--> MATLAB Matrix Algebra (Matlab Solutions)
http://nitroflare.com/view/254988EAB2EDAEF/bok%253A978-1-4842-0307-1.epub
MATLAB Matrix Algebra (Matlab Solutions) by Cesar Lopez
English | 14 Nov. 2014 | ISBN: 1484203089 | 240 Pages | EPUB | 1.08 MB
MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
MATLAB Matrix Algebra introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. Starting with a look at symbolic and numeric variables, with an emphasis on vector and matrix variables, you will go on to examine functions and operations that support vectors and matrices as arguments, including those based on analytic parent functions. Computational methods for finding eigenvalues and eigenvectors of matrices are detailed, leading to various matrix decompositions. Applications such as change of bases, the classification of quadratic forms and how to solve systems of linear equations are described, with numerous examples. A section is dedicated to sparse matrices and other types of special matrices. In addition to its treatment of matrices, you will also learn how MATLAB can be used to work with arrays, lists, tables, sequences and sets.
172 <--> MATLAB Symbolic Algebra and Calculus Tools
http://nitroflare.com/view/2B369D860C96B50/bok%253A978-1-4842-0343-9.epub
MATLAB Symbolic Algebra and Calculus Tools by Cesar Pérez Lopez
English | 10 Dec. 2014 | ISBN: 1484203445 | 260 Pages | EPUB | 1.78 MB
MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
MATLAB Symbolic Algebra and Calculus Tools introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. Starting with a look at symbolic variables and functions, you will learn how to solve equations in MATLAB, both symbolically and numerically, and how to simplify the results. Extensive coverage of polynomial solutions, inequalities and systems of equations are covered in detail. You will see how MATLAB incorporates vector, matrix and character variables, and functions thereof. MATLAB is a powerful symbolic manipulator which enables you to factorize, expand and simplify complex algebraic expressions over all common fields (including over finite fields and algebraic field extensions of the rational numbers). With MATLAB you can also work with ease in matrix algebra, making use of commands which allow you to find eigenvalues, eigenvectors, determinants, norms and various matrix decompositions, among many other features. Lastly, you will see how you can use MATLAB to explore mathematical analysis, finding limits of sequences and functions, sums of series, integrals, derivatives and solving differential equation.