Deep Learning Specialization on Coursera

Instructor: Andrew Ng

Organization: deeplearning.ai

Introduction

This repo contains all my work for this specialization. All the code base and images, are taken from Deep Learning Specialization on Coursera.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

In each section, the course description is provided as well the assignments and related course materials.

Skills Gained Throughout Specialization

• Tensorflow
• Convolutional Neural Network
• Artificial Neural Network
• Deep Learning
• Backpropogation
• Python Programming
• Hyperparameter
• Hyperparameter Optimization
• Machine Learning
• Inductive Transfer
• Multi-Task Learning
• Facial Recognition System

Programming Assignments

• Course 1: Neural Networks and Deep Learning:

  Objectives:

  • Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

  Code:

  • Course 1 Assignments

• Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

  Objectives:

  • Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow.

  Code:

  • Course 2 Assignments

• Course 3: Structuring Machine Learning Projects

  Objectives:

  • Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge.

  Code:

  • No Program Assigments

• Course 4: Convolutional Neural Networks

  Objectives:

  • Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

  Code:

  • Course 4 Assignments

• Course 5: Sequence Models Objectives:

  • Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

  Code:

  • Course 5 Assignments