𝟭. Generative AI by Microsoft: https://lnkd.in/eGiwKFK9
𝟮. MIT Efficient DL Computing: https://lnkd.in/e5EPBA7N
𝟯. NLP by UT Austin: https://lnkd.in/egHapvKh
𝟰. Deep Learning by Sebastian: https://lnkd.in/e9BvvAVS
𝟱. LLMs Bootcamp: https://lnkd.in/eTSP4yvr
𝟲. Harvard Intro to Python: https://lnkd.in/eNbM8vH5
𝟳. Deep Learning for Coders: https://lnkd.in/eiPyWeUn
𝟴. MIT Intro to Deep Learning: https://lnkd.in/edZkbPGK
𝟵. Neural Networks by Karpathy: https://lnkd.in/e5u9g4tb
𝟭𝟬. Prompt Engineering Guide: https://lnkd.in/gNbUWgbi
𝟭𝟭. Computer Vision Practice: https://lnkd.in/ebuKyu36
𝟭𝟮. Advanced NLP: https://lnkd.in/e_EFSxJP
𝟭𝟯. ML Stanford University: https://lnkd.in/eF7PWJWK
𝟭𝟰. ML Engineering Andrew Ng: https://lnkd.in/erxeJmeG
𝟭𝟱. Multimodal ML Carnegie: https://lnkd.in/eKWp8GEy
𝟭𝟲. Stanford Deep Meta: https://lnkd.in/eGjDuph7
𝟭𝟳. Stanford NLU: https://lnkd.in/ejxPDq-T
𝟭𝟴. Stanford Transformers: https://lnkd.in/ersjDW4U
𝟭𝟵. DL by Yann LeCun: https://lnkd.in/eHHcK4dn
𝟮𝟬. NLP by Hugging Face: https://lnkd.in/eMJqNb3N
𝟮𝟭. Deep Learning by d2l_ai: https://d2l.ai/index.html
22. Deeepmind × UCL RL: https://lnkd.in/eXuMQ2Y2
𝟮𝟯. Stanford NLP + DL: https://lnkd.in/eUfyVpKK
𝟮𝟰. Deepmind × UCL DL: https://lnkd.in/e8PASqcC
𝟮𝟱. Advanced Linear Algebra: https://lnkd.in/eqETXvej
𝟮𝟲. Stanford AI Techniques: https://lnkd.in/eNhueWKJ
𝟮𝟳. Practical DL: https://lnkd.in/eBa9gXY9
𝟮𝟴. UMich DL for CV: https://lnkd.in/ehqm9szC
𝟮𝟵. Stanford DL for CV: https://lnkd.in/e6DrtaY
𝟯𝟬. Harvard Stats Probability: https://lnkd.in/e5BcxjM5