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.

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

• Course 4: Convolutional Neural Networks

  • Week 1 - PA 1 - Convolutional Model: Step by Step
  • Week 1 - PA 2 - Convolutional Model: application
  • Week 2 - PA 0 - Keras Tutorial - The Happy House
  • Week 2 - PA 1 - Residual Networks
  • Week 2 - Papers for read: ImageNet Classification with Deep Convolutional Neural Networks, Very Deep Convolutional Networks For Large-Scale Image Recognition
  • Week 3 - PA 1 - Car detection with YOLOv2
  • Week 3 - Papers for read: You Only Look Once: Unified, Real-Time Object Detection, YOLO
  • Week 4 - PA 1 - Art generation with Neural Style Transfer
  • Week 4 - PA 2 - Face Recognition for the Happy House