(2/4)The book covers topics such as:
✅ Foundation of machine learning (supervised and unsupervised learning, lost function, gradients algorithm, etc.)
✅ Shallow and deep neural network
✅ Convolutional networks
✅ Transformers
✅ Diffusion models
✅ Deep reinforcement learning
(4/4) Along with the book, course lecture slides and Python notebooks are available on the book website and GitHub, respectively.
🔗 https://github.com/udlbook/udlbook
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