Resources of deep learning courses by Andrew Ng
In this early morning of Super Bowl Day, I finally finished Deep Learning Specialization taught by Andrew Ng.
This specialization includes 5 modules:
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.
Week1: Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
Week2: Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
Week3: Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
Week4: Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
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
Week 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks
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)
Week 2: ML Strategy (2)
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
Week 2: Deep convolutional models: case studies
Week 3: Object detection
Week 4: Special applications: Face recognition & Neural style transfer
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
Week 3: Sequence models & Attention mechanism
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.
Deep Learning textbook: Ian Goodfellow and Yoshua Bengio and Aaron Courville
Cheat Sheets for Deep Learning
TensorFlow and Deep Learning without a PhD (LOL)
Introduction to Machine Learning
Advanced Introduction to Machine Learning
Machine Learning and Data Mining