Instructor: Andrew Ng

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

• Learn the foundations of Deep Learning
• Understand how to build neural networks
• Learn how to lead successful machine learning projects
• Learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
• Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
• Practice all these ideas in Python and in TensorFlow.

• Week 1 - Introduction to deep learning
• Week 2 - Neural Networks Basics
  • Notes 1 - Logistic Regression as a Neural Network
  • Notes 2 - Vectorization
  • Programming Assignment 1 - Python Basics with NumPy
  • Programming Assignment 2 - Logistic Regression with a Neural Network Mindset
• Week 3 - Shallow Neural Networks]
  • Notes - Shallow neural networks
  • Programming Assignment - Planar Data Classification with one hidden layer
• Week 4 - Deep Neural Networks
  • Notes - Deep neural networks
  • Programming Assignment 1 - Building your Deep Neural Network - Step by Step
  • Programming Assignment 2 - Deep Neural Network Application: Image Classification

• Week 1
  • Initialization
  • Regularization
  • Gradient Checking
• Week 2
  • Optimization
• Week 3
  • Tensorflow

• Week 1
• Week 2

• Week 1 - Convolutional Model- step by step
• Week 2 - ResNets
• Week 3 - Car detection for Autonomous Driving
• Week 4
  • Neural Style Transfer
  • Face Recognition

• Week 1

  • Building a Recurrent Neural Network - Step by Step
  • Dinosaur Island -- Character-level language model
  • Jazz improvisation with LSTM

• Week 2

  • Word Vector Representation
  • Emojify

• Week 3

  • Machine Translation
  • Trigger Word Detection

Contributions are welcome! For bug reports or requests please submit an issue.