Deep Learning Specialization on Coursera

Master Deep Learning, and Break into AI

Instructor: Andrew Ng

Introduction

This repo is continuation of all work done by @Kulbear for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. Focus here are making sure dependencies are all accounted for.

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

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

Getting Started

Installation

1. Clone git repository to local machine.

$ cd ~/Projects # or a directory of your choosing
$ git clone https://github.com/francisglee/deep-learning-coursera

2. Create virtual environment using python3 venv

$ python3 -m venv venv # creates virtual environment, venv, in parent directory
$ source venv/bin/activate # activate virtual environment

3. pip install requirements

(venv)$ pip install -r requirements.txt

4. Now run the kernel "self-install" script:

(venv)$ python3 -m ipykernel install --user --name=venv

5. Run jupyter notebook.

(venv)$ jupyter notebook

6. You should now be able to see your kernel in the IPython notebook menu: Kernel -> Change kernel.

Built with

1. Jupyter Notebooks
2. Numpy
3. Sklearn
4. Matplotlib
5. testCases