Neural Networks Tutorial 🧠: Math and Mechanics for LLM Foundations

Hack into neural networks guide – vectors to backprop, no fluff. Essential for understanding large language models.

Neural Networks Basics

• Neurons, layers, weights, biases – the building blocks.
• Types: Feedforward, CNN, RNN – when to use what.
• Activations, backpropagation guide, gradient descent hacks.
• Loss functions, regularization, optimizers for peak performance.

Why Neural Networks Matter in AI

Dive deep into how neural nets power LLMs. Got questions? What’s your biggest backprop struggle? 🤔

My Neural Networks Notes

• Medium: Demystifying Neural Networks A-Z

Top Neural Networks Resources

• Deep Learning Book
• Neural Nets Book
• Stanford CS231n
• MIT Intro to Deep Learning
• Karpathy’s Neural Nets: Zero to Hero
• 3Blue1Brown Video Series
• Attention Is All You Need Paper
• Karpathy’s Recipe for Training Neural Nets
• Beginner Intro to Neural Networks
• Gated Convolutional Networks Paper
• Deep Learning Overview Paper
• Distill.pub Articles
• TensorFlow Core Guide
• PyTorch Tutorials
• PyTorch NN Module

Keywords: neural networks tutorial, backpropagation guide, deep learning basics, AI neural nets, LLM foundations