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.
- 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.
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
- 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
Course 5: Sequence Models
- 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.
- CMU maching learning
- UBC maching learning
- Hunag-yi Lee videos