Demystify artificial intelligence from core linear regression and gradient descent to deep neural networks, attention mechanisms, tokenization, transformer architectures, and prompt engineering.
A clear guide to Artificial Intelligence (AI): narrow vs general AI, machine learning vs rules-based systems, neural networks, and modern applications.
Learn what Machine Learning (ML) is, how algorithms learn from data, supervised vs unsupervised learning, training vs inference, and real-world pipelines.
Explore Deep Learning: multi-layered neural networks, automated feature extraction, convolutional and recurrent networks, and why GPUs are essential.
An intuitive architectural guide to artificial neural networks: artificial neurons (perceptrons), weights, biases, ReLU activations, and backpropagation.
Understand Generative AI: how systems create new text, images, and audio from prompts, diffusion models, GANs, and multimodal reasoning.
Learn how Large Language Models (LLMs) work: tokenization, self-attention mechanisms, pre-training vs fine-tuning (RLHF), context windows, and embeddings.