Learning Materials for Enrolled Students1、Machine Learning/Pattern Recognition Basic Concept Learning Learn the basic concepts of classification, regression, supervised learning, unsupervised learning, loss function and so on. Chinese Reference Book: Machine Learning, edited by Zhou Zhihua. 2. Theoretical knowledge learning 2.1 Machine Learning and Deep Learning. Learning video: the course “Deep Learning” by Enda Wu. Some mathematical foundations and formula derivations for machine learning: a Bilibili whiteboard derivation. 2.2 Computer Vision Course. Chinese machine learning video: Li Hongyi, Introduction for Machine\Deep Learning. It is recommended to study the first few chapters in depth, which is very helpful in consolidating the foundation of deep neural networks (DNN). Subsequent chapters can be viewed selectively according to interest. The Applied Deep Learning section of the course details pre-training models and some models in Natural Language Processing (NLP), which is recommended. 2.3 Deep Learning Tutorials 2.4 Multi-modual Visual LLM Tutorials 2.5 Some Related Tasks of Computer Vision Semantic Segmentation Tutorials [Link] Classification & Detection Tutorials [Link] 3. Programming learning Recommended programming language: Python. 3.1 Python Standard Library. 3.2 Chinese python learning videos. itheima programmer. 4. Deep learning framework Self-taught pytorch/tensorflow, pytorch recommended. 4.1 Pytorch documentation It is recommended that the official English document is the main one (there may be a delay in updating the content of the Chinese document). 4.2 Pytorch usage It is recommended to study Bilibili station DeepLizard. the explanation is in-depth and paired with some hands-on projects. |