Become an AI Researcher Course – LLM, Math, PyTorch, Neural Networks, Transformers

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Welcome to the full course on becoming an AI Researcher. This course will guide you step-by-step, starting with the foundational mathematics essential for understanding modern AI, before diving into PyTorch fundamentals. You will then learn about the building blocks of AI, from simple neural networks to the complexities of multi-layer architectures. The course ends with an in-depth module on Transformers, the critical technology underpinning today's Large Language Models (LLMs) and generative AI. Course from @vukrosic. Github: Vuk on X: ❤️ Try interactive AI courses we love, right in your browser: (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ Introduction & Course Overview - 00:00:00 Welcome & Course Overview - 00:05:28 Requirements & Setup for the Course Module 1: Foundational Mathematics for AI Research - 00:10:48 Math Lesson: Functions (Linear, Quadratic, Cubic, Square Root) - 00:19:10 Math Lesson: Derivatives (Rate of Change) - 00:33:19 Math Lesson: Vectors (Magnitude, Dot Product, Normalization) - 00:46:07 Math Lesson: Gradients (Steepest Ascent/Descent, Partial Derivatives) - 00:55:03 Math Lesson: Matrices (Multiplication, Transpose, Identity) - 01:08:39 Math Lesson: Probability (Expected Value, Conditional Probability) Module 2: PyTorch Fundamentals - 01:19:19 START: PyTorch Fundamentals & Creating Tensors - 01:26:03 PyTorch Lesson: Reshaping and Viewing Tensors - 01:27:48 PyTorch Lesson: Squeezing and Unsqueezing Dimensions - 01:41:0