This is the code repository for Hands-On Python Deep Learning for Web by Anubhav Singh and Sayak Paul, published by Packt.
Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow
When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS).
This book covers the following exciting features:
- Explore deep learning models and implement them in your browser
- Design a smart web-based client using Django and Flask
- Work with different Python-based APIs for performing deep learning tasks
- Implement popular neural network models with TensorFlow.js
- Design and build deep web services on the cloud using deep learning
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
if (test expression)
{
Statement upon condition is true
}
Following is what you need for this book: This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.
With the following software and hardware list you can run all code files present in the book (Chapter 1-12).
| Chapter | Software required | OS required |
|---|---|---|
| 1-12 | Anaconda distribution of Python and other Python packages | 1 GB RAM minimum, 8 GB recommended 15 GB disk space |
| 1-12 | Code editor of your choice (Sublime Text 3 recommended) | 2 GB RAM |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Anubhav Singh, a web developer since before Bootstrap was launched, is an explorer of technologies, often pulling off crazy combinations of uncommon tech. An international rank holder in the Cyber Olympiad, he started off by developing his own social network and search engine as his first projects at the age of 15, which stood among the top 500 websites of India during their operational years. He's continuously developing software for the community in domains with roads less walked on. You can often catch him guiding students on how to approach ML or the web, or both together. He's also the founder of The Code Foundation, an AI-focused start-up. Anubhav is a Venkat Panchapakesan Memorial Scholarship awardee and an Intel Software Innovator.
Sayak Paul is currently with PyImageSearch, where he applies deep learning to solve real-world problems in computer vision and bring solutions to edge devices. He is responsible for providing Q&A support to PyImageSearch readers. His areas of interest include computer vision, generative modeling, and more. Previously at DataCamp, Sayak developed projects and practice pools. Prior to DataCamp, Sayak worked at TCS Research and Innovation (TRDDC) on data privacy. There, he was a part of TCS's critically acclaimed GDPR solution called Crystal Ball. Outside of work, Sayak loves to write technical articles and speak at developer meetups and conferences.
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