授课教师: Andrew Ng

内容:

• Week 1: Introduction to deep learning
• Week 2: Neural Networks Basics
• Week 3: Shallow neural networks
• Week 4: Deep Neural Networks

内容:

• Week 1: Practical aspects of Deep Learning(Initialization, Regularization and Gradient Check)
• Week 2: Optimization algorithms
• Week 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks (Introduction to Tensorflow)

内容:

• Week 1: ML Strategy (1)
• Week 2: ML Strategy (2)

内容:

• 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

内容:

• Week 1: Recurrent Neural Networks
• Week 2: Natural Language Processing & Word Embeddings
• Week 3: Sequence models & Attention mechanism

推荐课程笔记：http:https://kyonhuang.top/Andrew-Ng-Deep-Learning-notes/#/

课程主要分为两部分：校园讲座和在线讲座。在线部分就是由DeepLearning.ai制作的深度学习网络课程，包括所有的在线编程作业；校园讲座的内容与在线部分不重叠，一半lecture会讨论偏策略的内容，另一半讲了GANs、强化学习、聊天机器人等技术，整体难度大于在线课程部分。

课程主页：http:https://cs230.stanford.edu/

In-Class Lecture地址：http:https://onlinehub.stanford.edu/cs230

Online Lecture地址：https://www.coursera.org/specializations/deep-learning

1. Class introduction & Logistics
2. Deep Learning Intuition
3. Full-cycle of a Deep Learning Project
4. Adversarial Attacks / GANs
5. AI+ Healthcare
6. Deep Learning Project strategy - Case studies
7. Interpretability of Neural Network
8. Career Advice / Reading Research Papers
9. Deep Reinforcement Learning
10. Chatbots / Closing Remarks