Deep Learning Specialization on Coursera

Master Deep Learning, and Break into AI

This is my personal projects for the course. The course covers deep learning from begginer level to advanced. Through five interconnected courses, this develop a profound knowledge of the hottest AI algorithms, mastering deep learning from its foundations (neural networks) to its industry applications (Computer Vision, Natural Language Processing, Speech Recognition, etc.). Instructor: Andrew Ng, DeepLearning.ai

Course 1. [Neural Networks and Deep Learning]

1. Week1 - [Introduction to deep learning]
2. Week2 - [Neural Networks Basics]
3. Week3 - [Shallow neural networks]
4. Week4 - [Deep Neural Networks]

Course 2. [Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization]

1. Week1 - [Practical aspects of Deep Learning] - Setting up your Machine Learning Application - Regularizing your neural network - Setting up your optimization problem
2. Week2 - [Optimization algorithms]
3. Week3 - [Hyperparameter tuning, Batch Normalization and Programming Frameworks]

Course 3. [Structuring Machine Learning Projects]

1. Week1 - [Introduction to ML Strategy] - Setting up your goal - Comparing to human-level performance
2. Week2 - [ML Strategy (2)] - Error Analysis - Mismatched training and dev/test set - Learning from multiple tasks - End-to-end deep learning

Course 4. [Convolutional Neural Networks]

1. Week1 - [Foundations of Convolutional Neural Networks]
2. Week2 - [Deep convolutional models: case studies] - Papers for read: ImageNet Classification with Deep Convolutional Neural Networks, Very Deep Convolutional Networks For Large-Scale Image Recognition
3. [Week3 - Object detection] - Papers for read: You Only Look Once: Unified, Real-Time Object Detection, YOLO
4. Week4 - [Special applications: Face recognition & Neural style transfer] - Papers for read: DeepFace, FaceNet

Course 5. [Sequence Models]

1. Week1 - [Recurrent Neural Networks](
2. Week2 - [Natural Language Processing & Word Embeddings](
3. Week3 - [Sequence models & Attention mechanism]

for more Papers see The most cited deep learning papers

Learn Tensorflow and Deep Neural Network

• I recommend you a video course for learning tensorflow from Google here
• A good introduction about Deep Neural Network, download here
  
• Best results on standard dataset like MNIST, CIFAR-10/100, ILSVRC2012... here
  
• Keras Documentation Chinese Version here
  
• Deep Learning by Goodfellow here

Some Good Machine Learning Tutorial

• Expectation Maximization(EM) course by Xu Yida on Youtube
  

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Other useful links

• TensorFlow-Book
• stanford-tensorflow-tutorials
• deep-learning-models
• deep-learning-with-python-notebooks
• Deep-learning-tutorials
• TensorFlow-ML-Exercises
• Keras Examples