Andrej Karpathy-Style Comprehensive Transformers from Scratch for Research Foundations
Master the Transformer architecture from first principles, building a deep, intuitive understanding for advanced research and application in modern AI.
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The Sequence Problem: Why Traditional Models Struggle
Unit 1: Understanding Sequences
Unit 2: Traditional ML's Limitations
Unit 3: The Need for New Models
Recurrent Neural Networks (RNNs): A First Attempt at Sequences
Unit 1: Introducing Recurrent Neural Networks
Unit 2: The Core Mechanics of Simple RNNs
Unit 3: RNN Architectures and Applications
Unit 4: Training and Limitations of Simple RNNs
Unit 5: Conceptual Implementation of Simple RNNs
The Achilles' Heel of RNNs: Vanishing/Exploding Gradients
Unit 1: Understanding Gradient Flow in Neural Networks
Unit 2: The Vanishing Gradient Problem in RNNs
Unit 3: The Exploding Gradient Problem in RNNs
Unit 4: RNN Limitations and the Need for Alternatives
Convolutional Neural Networks (CNNs) for Sequences: Local Views
Unit 1: CNNs: A Quick Primer
Unit 2: CNNs for Sequences: 1D Convolutions
Unit 3: Limitations of CNNs for Sequences
The Parallelization Imperative: Why We Need a New Paradigm
Unit 1: The Need for Speed: Why RNNs Are Slow
Unit 2: Beyond RNNs: Seeking Parallel Solutions
Unit 3: Setting the Stage for Transformers
Attention: The Core Idea of 'Paying Attention'
Unit 1: Beyond Fixed Contexts
Query, Key, Value: The Building Blocks of Attention
Unit 1: Understanding the Core Components of Attention
Unit 2: Q, K, V in Action: From Words to Vectors
Unit 3: The Interplay of Q, K, and V
Dot-Product Attention: The Raw Score Calculation
Unit 1: The Essence of Dot Product Attention
Unit 2: Matrix Multiplication for Efficiency
Unit 3: Conceptualizing the Raw Scores
Scaled Dot-Product Attention: Stabilizing the Scores
Unit 1: The Need for Scaling Attention Scores
Unit 2: The Scaling Solution
Softmax and Weighted Sum: Turning Scores into Probabilities and Outputs
Unit 1: From Raw Scores to Meaningful Weights
Unit 2: The Weighted Sum: Combining Information
Self-Attention: Attending to Oneself
Unit 1: Understanding Self-Attention
Unit 2: Deep Dive into Self-Attention Mechanics
Unit 3: Self-Attention in Context
Implementing Scaled Dot-Product Self-Attention from Scratch (Conceptual)
Unit 1: Setting the Stage for Self-Attention
Unit 2: Calculating Attention Scores
Unit 3: Producing the Attention Output
Multi-Head Attention: Diverse Perspectives
Unit 1: Beyond a Single Focus
Unit 2: The Multi-Head Mechanism
Unit 3: Finalizing Multi-Head Output
Implementing Multi-Head Attention from Scratch (Conceptual)
Unit 1: Setting the Stage for Multi-Head Attention
Unit 2: Independent Projections for Each Head
Unit 3: Parallel Attention Computations
Unit 4: Combining Head Outputs
Unit 5: Putting It All Together
Positional Encoding: Injecting Order into Orderless Attention
Unit 1: The Need for Position
Unit 2: Types of Positional Encodings
Unit 3: Sinusoidal Positional Encoding
Unit 4: Learned Positional Embeddings
Unit 5: Integrating Positional Encoding
Sinusoidal Positional Encoding: A Deterministic Approach
Unit 1: The Need for Position
Unit 2: Introducing Sinusoidal Encoding
Unit 3: Properties and Advantages
Unit 4: Integration and Alternatives
Unit 5: Practical Considerations
Learned Positional Embeddings: An Alternative Strategy
Unit 1: Understanding Learned Positional Embeddings
