• ******Deep Learning MIT - Best Practical and in Depth Coverage & Overview - https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&si=UDgWyDLKWPKN4Zs3
• https://nthu-datalab.github.io/ml/index.html
• https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA
• https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4

https://deep-learning-s25.vercel.app/

Learning Paradigms

• Paradigms - Must

• Probabilistic Modelling & Ai Agents

• Other Learning Paradigms

• Evolutionary Algorithms

Deep Learning Algorithms :

1. Multilayer Perceptrons (MLPs)

2. Autoencoders

3. Convolutional Neural Networks (CNNs)

   https://www.youtube.com/watch?v=ftc7rj7kzQ0

   https://medium.com/@zurister/depth-wise-convolution-and-depth-wise-separable-convolution-37346565d4ec

4. Recurrent Neural Networks (RNNs)

5. Long Short Term Memory Networks (LSTMs)

6. Generative Adversarial Networks (GANs)

7. Radial Basis Function Networks (RBFNs)

8. Self Organizing Maps (SOMs)

9. Deep Belief Networks (DBNs)

10. Restricted Boltzmann Machines( RBMs)

11. Transformers https://www.k-a.in/llm3.html

    https://nlp.seas.harvard.edu/annotated-transformer/