Become an AI Researcher: Full Course on LLMs, Math, PyTorch, Neural Networks & Transformers

Course Roadmap and Foundations

• 🗺️ This comprehensive course guides aspiring AI researchers step-by-step, starting with foundational mathematics.
• 🐍 Prerequisites include basic Python knowledge, with resources provided for setup and environment configuration.
• 📚 The curriculum covers mathematics, PyTorch fundamentals, neural networks, and transformers, aiming to equip learners to read and contribute to AI research papers.

Module 1: Foundational Mathematics

• 📈 Functions are introduced, including linear, quadratic, cubic, and square root types, explaining how coefficients affect their shape and behavior.
• 📉 Derivatives are explained as the rate of change of a function, illustrating how they represent the slope at any given point and the rules for calculating them.
• 🌌 Vectors are presented as arrays of numbers representing magnitude and direction, with explanations on addition, scalar multiplication, and calculating length.
• ⛰️ Gradients are defined as the direction of steepest ascent, derived from partial derivatives, crucial for minimizing error in neural networks.
• 🧮 Matrices are detailed as arrays of arrays, emphasizing matrix multiplication as a core operation in neural networks, along with addition and scalar multiplication.
• 🎲 Probability concepts are covered, including basic probability, conditional probability, expected value, and the law of large numbers, essential for understanding AI models.

Module 2: PyTorch Fundamentals

• 💡 Tensors are introduced as the fundamental data structure in PyTorch, akin to multi-dimensional arrays.
• 🛠️ Key tensor operations like flattening, reshaping, viewing, squeezing, and unsqueezing are demonstrated for data manipulation.
• 🗂️ Indexing and slicing techniques are shown for accessing and manipulating specific parts of tensors.
• 🔢 Special tensors such as zeros, ones, and random tensors are covered, along with converting between NumPy arrays and PyTorch tensors.

Module 3: Neural Networks

• 🧠 A single neuron is explained with its weights, biases, and weighted sum, forming the basic unit of a neural network.
• 📈 Activation functions like Sigmoid and ReLU are introduced to introduce non-linearity, enabling networks to learn complex patterns.
• 🧱 Multi-layer networks and backpropagation are discussed, explaining how errors are propagated backward to update weights and train the network.

Module 4: Transformers for LLMs

• 🚀 Transformers are presented as the architecture underpinning modern Large Language Models (LLMs) and generative AI.
• 🔍 The attention mechanism is detailed, explaining how models weigh the importance of different input tokens (Query, Key, Value) to understand context.
• 🧩 Self-attention and multi-head attention are explored, allowing models to focus on different parts of the input simultaneously.
• 📍 Rotary Positional Embeddings (RoPE) are introduced for encoding the order of tokens within a sequence.
• 🧱 The Transformer block is described, including feed-forward networks and normalization layers, forming the core of the architecture.
• 📝 Tokenization is explained as the process of converting text into numerical representations for LLMs.