README
The nitty-gritty, under-the-hood things about AI and neural networks, covering things like architecture and ML.
#ai #ai/conceptual #readme
Table of contents
- Activation Functions
- Explanation of LLMs Being Few-shot Learners + How Tools are Provided to LLMs
- Loss Functions
- ML (Backpropogation)
- MLPs (Feed-Forward Networks)
- Mixture of Experts - A Brief
- Neurons
- RNNs, CNNs, Bag of Words, Bigrams and More
- Scalars, Vectors, Matrices, and Tensors; How Tensors and other Data Types > 1D Get Used in MLP
- Transformers and Modern LLMs