neurology

AI Fundamentals — Deep Dive

From perceptrons to transformers. Each chapter: visual journey overview + under-the-hood deep dive.
Co-Created by Kiran Shirol and Claude
Core Topics Neural Networks Deep Learning Transformers LLMs Generative AI ML Paradigms
home Learning Portal play_arrow Start Learning summarize Key Insights dictionary Glossary 14 chapters · Each with High Level + Under the Hood
Origins

What Is AI, History & ML Paradigms

Definitions, the 70-year journey, learning paradigms, and data fundamentals.
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What Is Artificial Intelligence?
Narrow vs general AI, symbolic AI, connectionism, and the spectrum of intelligence.
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History of AI
From Turing’s 1950 paper through AI winters to the deep learning revolution.
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Machine Learning Paradigms
Supervised, unsupervised, and reinforcement learning — how machines learn.
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Data & Feature Engineering
Cleaning, normalization, encoding, feature selection, and train/test splits.
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Neural Nets

Perceptrons, Training, CNNs & RNNs

The building blocks of deep learning — from single neurons to sequence models.
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The Perceptron & Neural Network Basics
Weights, biases, activation functions, and the universal approximation theorem.
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Training Neural Networks
Loss functions, gradient descent, backpropagation, and modern optimizers.
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Convolutional Neural Networks
Convolution, pooling, LeNet to ResNet — the computer vision revolution.
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Recurrent Neural Networks & Sequences
RNNs, LSTMs, GRUs, vanishing gradients, and gating mechanisms.
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Modern AI

Transformers, LLMs, GenAI & RL

The architectures and techniques driving today’s AI revolution.
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Attention & Transformers
“Attention Is All You Need” — self-attention, multi-head, and positional encoding.
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Large Language Models
Tokenization, pretraining, emergent abilities, scaling laws, and GPT evolution.
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Generative AI
GANs, VAEs, diffusion models — from StyleGAN to DALL·E and Stable Diffusion.
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Reinforcement Learning Deep Dive
Q-learning, policy gradients, PPO, RLHF — from Atari DQN to AlphaGo.
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Outlook

Ethics & The AI Landscape Today

Responsible AI, bias, and where the field is heading next.
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AI Ethics & Bias
Fairness, accountability, transparency, and frameworks for responsible AI.
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The AI Landscape Today
Foundation models, multimodal AI, agents, edge deployment, and what’s next.
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