Gary Jackson

I'm a software engineer based in Brisbane. I like understanding things at the implementation level, which usually means building them from scratch to see how they actually work. Right now that's look

Brisbane Joined on Jul 27, 2021 https://www.garyjackson.dev/

Gary Jackson

Apr 29

Chapter 10: Multi-Head Attention and the MLP Block

#csharp #machinelearning #transformers #tutorial

7 min read

Gary Jackson

Apr 28

Chapter 9: Single-Head Attention - Tokens Looking at Each Other

#csharp #machinelearning #transformers #tutorial

9 min read

Gary Jackson

Apr 27

Chapter 8: RMS Normalisation and Residual Connections

#csharp #machinelearning #transformers #tutorial

4 min read

Gary Jackson

Apr 26

Chapter 7: The Training Loop and Adam Optimiser

#csharp #machinelearning #transformers #tutorial

7 min read

Gary Jackson

Apr 25

Chapter 6: Embeddings, the Forward Pass, and the Loss Function

#csharp #machinelearning #transformers #tutorial

7 min read

Gary Jackson

Apr 24

Chapter 5: Linear Transformation and Softmax

#csharp #machinelearning #transformers #tutorial

4 min read

Gary Jackson

Apr 23

Chapter 4: The Bigram Model - Simplest Possible Language Model

#csharp #machinelearning #transformers #tutorial

5 min read

Gary Jackson

Apr 22

Chapter 3: The Tokenizer - Text to Numbers and Back

#csharp #machinelearning #transformers #tutorial

2 min read

Gary Jackson

Apr 21

Chapter 2: Backward - Automatic Gradient Computation

#csharp #machinelearning #transformers #tutorial

7 min read

Gary Jackson

Apr 21

Chapter 1: The Value Class - Recording the Forward Pass

#csharp #machinelearning #transformers #tutorial

10 min read

Gary Jackson

Apr 21

Chapter 0: Project Setup

#csharp #machinelearning #transformers #tutorial

4 min read

Gary Jackson

Apr 21

Building a GPT From Scratch in C# - Introduction

#csharp #machinelearning #transformers #tutorial

3 min read

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Gary Jackson

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Chapter 10: Multi-Head Attention and the MLP Block

Chapter 9: Single-Head Attention - Tokens Looking at Each Other

Chapter 8: RMS Normalisation and Residual Connections

Chapter 7: The Training Loop and Adam Optimiser

Chapter 6: Embeddings, the Forward Pass, and the Loss Function

Chapter 5: Linear Transformation and Softmax

Chapter 4: The Bigram Model - Simplest Possible Language Model

Chapter 3: The Tokenizer - Text to Numbers and Back

Chapter 2: Backward - Automatic Gradient Computation

Chapter 1: The Value Class - Recording the Forward Pass

Chapter 0: Project Setup

Building a GPT From Scratch in C# - Introduction