In this early morning of Super Bowl Day, I finally finished Deep Learning Specialization taught by Andrew Ng.

This specialization includes 5 modules:

Course 1: Neural Networks and Deep Learning

Course 2: Improving Deeping Neural Networks

Understand industry best-practices for building deep learning applications.
Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking.
Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance.
Be able to implement a neural network in TensorFlow.
- Week 1: Practical aspects of Deep Learning
- Week 2: Optimization algorithms
  - Optimization
- Week 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks
  - Tensorflow Tutorial

Understand how to diagnose errors in a machine learning system.
Be able to prioritize the most promising directions for reducing error.
Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance.
Know how to apply end-to-end learning, transfer learning, and multi-task learning.
- Week 1: ML Strategy (1)
  - Bird recognition
- Week 2: ML Strategy (2)
  - Autonomous driving

Understand how to build a convolutional neural network, including recent variations such as residual networks.
Know how to apply convolutional networks to visual detection and recognition tasks.
Know to use neural style transfer to generate art.
Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
- Week 1: Foundations of Convolutional Neural Networks
  - Convolutional Model: step by step
  - Convolutional model: application
- Week 2: Deep convolutional models: case studies
  - Residual Networks
- Week 3: Object detection
  - Car detection with YOLOv2
- Week 4: Special applications: Face recognition & Neural style transfer
  - Art generation with Neural Stype Transfer
  - Face Recognition for the Happy House

Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
Be able to apply sequence models to natural language problems, including text synthesis.
Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
- Week 1: Recurrent Neural Networks
- Week 2: Natural Language Processing & Word Embeddings
  - Operation on word vectors - Debiasing
  - Emojify: Sentimental analysis
- Week 3: Sequence models & Attention mechanism
  - Neural Machine Translation with Attention
  - Trigger word detection

Please see my GitHub for details.

I have also reviewed two amazing courses offered by Stanford University, which are

For the basic of machine learning, please refer to Andrew’s Maching Learning on Coursera and CS229: Maching Learning. Here is my GitHub repo for Andrew’s Machine Learning course as guidance if needed.