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

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

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]

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

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

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

• 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

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

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