Zero to Hero — Master every mathematical concept behind AI Transformers and LLMs like ChatGPT. With cricket analogies and bilingual content.
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Before we build AI, we need a shared language — the language of mathematics. This module covers the basic building blocks that every AI formula uses.
Vectors and matrices are the data structures of AI. Every piece of data — text, images, audio — gets converted into vectors and processed through matrices.
Eigenvalues, SVD, and transformations — the deeper math that powers dimensionality reduction and understanding how neural networks transform data.
Derivatives and the chain rule — the math that makes neural networks learn. Without calculus, there is no training.
How AI models actually learn — gradient descent, learning rates, and modern optimizers like Adam that power every LLM.
AI makes predictions under uncertainty. Probability and statistics give us the tools to reason about uncertainty, make decisions, and evaluate models.
Entropy, cross-entropy, and KL divergence — the mathematics of measuring information and uncertainty. These directly define how language models are trained.
How a neural network computes, from a single neuron to deep networks. Activation functions, forward pass, and the legendary backpropagation algorithm.
How AI converts words, images, and data into numbers — the mathematical magic of embeddings that makes language models possible.
Before Transformers, RNNs and LSTMs were kings of sequential data. Understanding their limitations reveals why the Transformer was revolutionary.
The breakthrough idea that powers all modern AI. Attention lets models focus on the most relevant parts of the input, enabling understanding of context and relationships.
The architecture that changed everything. Multi-head attention, positional encoding, layer normalization — every piece of the puzzle that makes GPT, ChatGPT, and all modern LLMs work.
Softmax, cross-entropy loss, temperature, sampling strategies — the math behind how GPT learns to generate text and how we control its output.
The final module — scaling laws, how GPT became ChatGPT, and the mathematical principles that will shape the future of AI.