Deep Learning Basics#

This part covers the advanced part of machine learning - the basics of deep learning. Please be familiar with the use of the deep learning library Pytorch, and understand the key principles and models of deep learning. Pay particular attention to the following highlights in Deep Learning Fundamentals:

• Feedforward Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Encoder, Decoder, Attention
• Regularization
• Optimizers

If you have never used Pytorch#

Please refer to the following tutorials:

• Pytorch official practice project
• Chinese Documentation Tutorial: Alibaba Cloud's Pytorch Tutorial
• English documentation tutorial: Machine Learning Pytorch Tutorial
• Pytorch's common code Cheat Sheet

When learning deep learning knowledge, we recommend that you adopt code-first and learn through practical code#

Please refer to the following tutorials:

• Online reading of Chinese books with additional code samples: hands-on deep learning
• English video tutorial: Practical Deep Learning for Coders (fast.ai）
• English video tutorial: PyTorch for Deep Learning
• (Optional) Pytorch official English documentation: Deep Learning with Pytorch

If you have ever been exposed to deep learning#

Please refer to the following links to review important knowledge points:

• Chinese documentation: Chinese Notes for Andrew Ng's Deep Learning Tutorial

If you want to add more theoretical knowledge of deep learning#

Please refer to the following tutorials:

• Book in English: Deep Learning (MIT)

If you need more advice on training neural networks#

Please refer to:

• Initializing Neural Networks
• Parameter Optimizing in Neural Networks