Artificial Intelligence Guides

Computer Vision Basics How machines “see”: pixels and tensors, convolutional neural networks, essential tasks (classification, detection, segmentation, keypoints), evaluation, data strategy, deployment on web/mobile/edge, and pitfalls to avoid. TL;DR: Computer Vision (CV) converts images and video into structure and decisions. Modern CV uses convolutional neural networks (and increasingly transformers) trained on labeled images, then fine-tuned […]

Natural Language Processing (NLP) Explained Tokens, embeddings, classic methods, transformers, evaluation, safety, and how to build language features that users actually trust. TL;DR: NLP turns raw text into structure and meaning. Early pipelines used bag-of-words and n-grams with linear models; modern systems lean on transformers and large language models (LLMs) that understand context via attention.

Basics of Neural Networks From the intuition of artificial “neurons” to training with backpropagation, regularization, and modern architectures. this chapter demystifies neural nets and shows when they help (and when simpler models win). TL;DR: A neural network stacks layers of simple units that apply weights, add biases, and pass values through nonlinear activations. By composing

Introduction to Machine Learning (Supervised vs Unsupervised) A practical, hands-on tour of machine learning: what it is, a short history, how models learn, the difference between supervised and unsupervised learning, how to evaluate systems, and how to ship something useful without drowning in jargon. TL;DR: Machine Learning (ML) learns patterns from data to make predictions