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Comments on Source
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GitHub - DoctorWkt/acwj: A Compiler Writing Journey
This GitHub repository documents the author's journey to create a self-compiling compiler for a subset of the C language. The author shares steps taken and explanations to help others follow along practically. The author credits Nils M Holm's SubC compiler for inspiration and differentiates their code with separate licensing.
Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine learning.
The exercises are on the following topics: linear algebra, optimisation,
directed graphical models, undirected graphical models, expressive power of
graphical models, factor graphs and message passing, inference for hidden
Markov models, model-based learning (including ICA and unnormalised models),
sampling and Monte-Carlo integration, and variational inference.
The Random Transformer
This blog post provides an end-to-end example of the math within a transformer model, with a focus on the encoder part. The goal is to understand how the model works, and to make it more manageable, simplifications are made and the dimensions of the model are reduced. The post recommends reading "The Illustrated Transformer" blog for a more intuitive explanation of the transformer model. The prerequisites for understanding the content include basic knowledge of linear algebra, machine learning, and deep learning. The post covers the math within a transformer model during inference, attention mechanisms, residual connections and layer normalization, and provides some code to scale it up.
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