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

  • Understand the major technology trends driving Deep Learning.
  • Be able to build, train and apply fully connected deep neural networks.
  • Know how to implement efficient (vectorized) neural networks.
  • Understand the key parameters in a neural network’s architecture.

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.

Course 3: Sturcturing Machine Learning Projects

  • 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.

Course 4: Convolutional Neural Networks

Course 5: Sequence Models

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.

Other Resources

Deep Learning textbook: Ian Goodfellow and Yoshua Bengio and Aaron Courville

Cheat Sheets for Deep Learning

Deep Learning Projects

TensorFlow and Deep Learning without a PhD (LOL)

  • CMU maching learning

Introduction to Machine Learning

Advanced Introduction to Machine Learning

  • UBC maching learning

Machine Learning and Data Mining

Machine Learning

  • Hunag-yi Lee videos

Maching Learning and Deep Learning resources