Deep Learning Specialization on Coursera (offered by deeplearning.ai)

Programming assignments and quizzes from all courses in the Coursera Deep Learning specialization offered by deeplearning.ai.

Instructor: Andrew Ng

Notes

For detailed interview-ready notes on all courses in the Coursera Deep Learning specialization, refer www.aman.ai.

Cloning Instructions

1. git-lfs is used to handle large dataset files in this repo. As such, please make sure git-lfs is installed before cloning this repo.
2. Steps to install git-lfs: curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash sudo apt-get install git-lfs
3. Clone repo: git clone <repo_path>
4. Run setup.sh to (i) download a pre-trained VGG-19 dataset and (ii) extract the zip'd pre-trained models and datasets that are needed for all the assignments.

Note that if you git clone'd before installing git-lfs (which downloaded only pointers to lfs files), install git-lfs and then run git lfs pull.

Credits

This repo contains my work for this specialization. The code base, quiz questions and diagrams are taken from the Deep Learning Specialization on Coursera, unless specified otherwise.

Programming Assignments

Course 1: Neural Networks and Deep Learning

• Week 2 - PA 1 - Python Basics with Numpy
• Week 2 - PA 2 - Logistic Regression with a Neural Network mindset
• Week 3 - PA 3 - Planar data classification with one hidden layer
• Week 4 - PA 4 - Building your Deep Neural Network: Step by Step
• Week 4 - PA 5 - 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 are no PAs for this course. But this course comes with very interesting case study quizzes (below).

Course 4: Convolutional Neural Networks

• Week 1 - PA 1 - Convolutional Model: step by step
• Week 1 - PA 2 - Convolutional Neural Networks: Application
• Week 2 - PA 1 - Keras - Tutorial - Happy House
• Week 2 - PA 2 - Residual Networks
• Week 3 - PA 1 - Car detection with YOLO for Autonomous Driving
• Week 4 - PA 1 - Art Generation with Neural Style Transfer
• Week 4 - PA 2 - Face Recognition

Course 5: Sequence Models

• Week 1 - PA 1 - Building a Recurrent Neural Network - Step by Step
• Week 1 - PA 2 - Dinosaur Land -- Character-level Language Modeling
• Week 1 - PA 3 - Jazz improvisation with LSTM
• Week 2 - PA 1 - Word Vector Representation and Debiasing
• Week 2 - PA 2 - Emojify!
• Week 3 - PA 1 - Neural Machine Translation with Attention
• Week 3 - PA 2 - Trigger Word Detection

Quiz Solutions

Course 1: Neural Networks and Deep Learning

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

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

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

Course 3: Structuring Machine Learning Projects

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

Course 4: Convolutional Neural Networks

• Week 1 Quiz - The basics of ConvNets: Text | PDF
• Week 2 Quiz - Deep convolutional models: Text | PDF
• Week 3 Quiz - Detection algorithms: Text | PDF
• Week 4 Quiz - Special applications: Face recognition & Neural style transfer: Text | PDF

Course 5: Sequence Models

• Week 1 Quiz - Recurrent Neural Networks: Text | PDF
• Week 2 Quiz - Natural Language Processing & Word Embeddings: PDF
• Week 3 Quiz - Sequence models & Attention mechanism: Text | PDF

(Unofficial) lecture notes

Credits to Mahmoud Badry for the unofficial lecture notes.

Disclaimer

I recognize the hard time people spend on building intuition, understanding new concepts and debugging assignments. The solutions uploaded here are only for reference. They are meant to unblock you if you get stuck somewhere. I hope you don't copy any part of the code as-is (the programming assignments are fairly easy if you read the instructions carefully). Similarly, try out the quizzes yourself before you refer to the quiz solutions. This course is the most straight-forward deep learning course I have ever taken, with fabulous course content and structure. It's a treasure by the deeplearning.ai team.