README

The nitty-gritty, under-the-hood things about AI and neural networks, covering things like architecture and ML.

#ai #ai/conceptual #readme

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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