Deep Learning Specialization

A bottom-up approach to deep learning, by Prof. Andrew Ng.

1. Neural Networks and Deep Learning

• Week 2
  • Python Basics with Numpy
  • Logistic Regression as a Neural Network
• Week 3
  • Planar data classification with one hidden layer
• Week 4
  • Building your Deep Neural Network - Step by Step
  • Deep Neural Network Application: Image Classification

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

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

3. Structuring Machine Learning Projects

No programming assignment. Andrew gave some practical advise based on his years of experience working in this industry.

4. Convolutional Neural Networks

• Week 1
  • Convolution model - Step by Step
  • Convolution model - Application
• Week 2
  • Keras Tutorial
  • ResNets
• Week 3
  • Car detection for Autonomous Driving
• Week 4
  • Face Recognition
  • Neural Style Transfer

5. Sequence Models

• Week 1
  • Building a recurrent neural network - step by step
  • Dinosaur Island - Character-Level Language Modeling
  • Jazz improvisation with LSTM
• Week 2
  • Operations on word vectors - Debiasing
  • Emojify
• Week 3
  • Neural Machine Translation with Attention
  • Trigger word detection

Author

© Rui Liu