Deep Learning Specialization on Coursera

Master Deep Learning, and Break into AI

Instructor: Andrew Ng

Introduction

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

What I want to say

VERBOSE CONTENT WARNING: YOU CAN JUMP TO THE NEXT SECTION IF YOU WANT

As a CS major student and a long-time self-taught learner, I have completed many CS related MOOCs on Coursera, Udacity, Udemy, and Edx. I do understand the hard time you spend on understanding new concepts and debugging your program. There are discussion forums on most MOOC platforms, however, even a question with detailed description may need some time to be answered. Here I released these solutions, which are only for your reference purpose. It may help you to save some time. And I hope you don't copy any part of the code (the programming assignments are fairly easy if you read the instructions carefully), see the quiz solutions before you start your own adventure. This course is almost the simplest deep learning course I have ever taken, but the simplicity is based on the fabulous course content and structure. It's a treasure given by deeplearning.ai team.

Currently, this repo has 3 major parts you may be interested in and I will give a list here.

Programming Assignments

• Course 1: Neural Networks and Deep Learning

  • Week 2 - PA 1 - Logistic Regression with a Neural Network mindset
  • Week 3 - PA 2 - Planar data classification with one hidden layer
  • Week 4 - PA 3 - Building your Deep Neural Network: Step by Step¶
  • Week 4 - PA 4 - Deep Neural Network for Image Classification: Application

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

  • Week 1 - PA 1 - Initialization
  • Week 1 - PA 2 - Regularization
  • Week 1 - PA 3 - Gradient Checking
  • Week 2 - PA 4 - Optimization Methods
  • Week 3 - PA 5 - TensorFlow Tutorial

• Course 3: Structuring Machine Learning Projects

  • There is no PA for this course. But this course comes with very interesting case study quizzes.

• Course 4: Convolutional Neural Networks

  • Week 1 - PA 1 - Convolutional Model: step by step
  • Week 1 - PA 2 - Convolutional Model: application
  • Week 2 - PA 1 - Keras - Tutorial - Happy House
  • Week 2 - PA 2 - Residual Networks

• Course 5: Sequence Models

  • Week 1 - PA 1 - Building a Recurrent Neural Network - Step by Step
  • Week 1 - PA 2 - Character level language model - Dinosaurus land

Quiz Solutions

There are concerns that some people may use the content here to quickly ace the course so I'll no longer update any quiz solution.

• Course 1: Neural Networks and Deep Learning

  • Week 1 Quiz - Introduction to deep learning
  • Week 2 Quiz - Neural Network Basics
  • Week 3 Quiz - Shallow Neural Networks
  • Week 4 Quiz - Key concepts on Deep Neural Networks

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

  • Week 1 Quiz - Practical aspects of deep learning
  • Week 2 Quiz - Optimization algorithms
  • Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks

• Course 3: Structuring Machine Learning Projects

  • Week 1 Quiz - Bird recognition in the city of Peacetopia (case study)
  • Week 2 Quiz - Autonomous driving (case study)

- Course 4: Convolutional Neural Networks - Course 5: Sequence Models

## Important Slide Notes

I screenshotted some important slide page and store them into GitHub issues. It seems not very helpful for everyone since I only keep those I think may be useful to me.

- Screenshots for Course 1: Neural Networks and Deep Learning

- Screenshots for Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

- Screenshots for Course 3: Structuring Machine Learning Projects

- Screenshots for Course 4: Convolutional Neural Networks

- Screenshots for Course 5: Sequence Models

Milestones

• 2017-08-17: Finished the first-released 3 courses, YAY! 😈