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A public note repository of everything I've read thus far

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.obsidian
.obsidian
 
 
assets
assets
 
 
.gitignore
.gitignore
 
 
60 Minute Blitz into Pytorch.md
60 Minute Blitz into Pytorch.md
 
 
A Comprehensive Overview of Large Language Models.md
A Comprehensive Overview of Large Language Models.md
 
 
A Guide To Hyperparameters.md
A Guide To Hyperparameters.md
 
 
A Practical Survey on Faster and Lighter Transformers.md
A Practical Survey on Faster and Lighter Transformers.md
 
 
A Short Introduction To Machine Learning.md
A Short Introduction To Machine Learning.md
 
 
An Introduction to Deep Reinforcement Learning.md
An Introduction to Deep Reinforcement Learning.md
 
 
Atomised Search Length.md
Atomised Search Length.md
 
 
Attention in RNNs.md
Attention in RNNs.md
 
 
Building Recommendation Systems.md
Building Recommendation Systems.md
 
 
Chroma DB Chunking Guide.md
Chroma DB Chunking Guide.md
 
 
Designing Machine Learning Systems.md
Designing Machine Learning Systems.md
 
 
Elicit Machine Learning Guide.md
Elicit Machine Learning Guide.md
 
 
How we built Text-to-SQL at Pinterest.md
How we built Text-to-SQL at Pinterest.md
 
 
Improving Language Understanding by Generative Pre-Training.md
Improving Language Understanding by Generative Pre-Training.md
 
 
Information Retrieval.md
Information Retrieval.md
 
 
JAX.md
JAX.md
 
 
Kyle Corbitt - From Prompt To Fine Tuning.md
Kyle Corbitt - From Prompt To Fine Tuning.md
 
 
LLM Fine Tuning Maven.md
LLM Fine Tuning Maven.md
 
 
Language Models Are Unsupervised Multitask Learners.md
Language Models Are Unsupervised Multitask Learners.md
 
 
Large Language Models - Evals in the Wild.md
Large Language Models - Evals in the Wild.md
 
 
Large Language Models.md
Large Language Models.md
 
 
LoRA Learns Less and Forgets Less.md
LoRA Learns Less and Forgets Less.md
 
 
MS-Shift An Analysis of MS Marco Distribution Shifts on Neural Retrieval.md
MS-Shift An Analysis of MS Marco Distribution Shifts on Neural Retrieval.md
 
 
Machine Learning with PyTorch and Scikit-Learn.md
Machine Learning with PyTorch and Scikit-Learn.md
 
 
Matryoshka Embeddings.md
Matryoshka Embeddings.md
 
 
ORPO Monolithic Preference Optimization without Reference Model.md
ORPO Monolithic Preference Optimization without Reference Model.md
 
 
Pandas.md
Pandas.md
 
 
Papers.md
Papers.md
 
 
Principal Component Analysis.md
Principal Component Analysis.md
 
 
Pytorch.md
Pytorch.md
 
 
README.md
README.md
 
 
RLHF.md
RLHF.md
 
 
RNN.md
RNN.md
 
 
Ranking.md
Ranking.md
 
 
Recommendation Systems.md
Recommendation Systems.md
 
 
Representations.md
Representations.md
 
 
Retrieval Augmented Generation.md
Retrieval Augmented Generation.md
 
 
Routing.md
Routing.md
 
 
The Annotated Transformer.md
The Annotated Transformer.md
 
 
The Illustrated GPT-2 - Visualising Transformer Language Models.md
The Illustrated GPT-2 - Visualising Transformer Language Models.md
 
 
The Illustrated Transformer.md
The Illustrated Transformer.md
 
 
The Spelled Out Intro to Neural Networks and Back Propagation.md
The Spelled Out Intro to Neural Networks and Back Propagation.md
 
 
The Transformer Explained.md
The Transformer Explained.md
 
 
What is a GPT - A visual intro to transformers.md
What is a GPT - A visual intro to transformers.md
 
 
t-SNE.md
t-SNE.md
 
 

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These are my existing ML notes which are a record of all the things I've learnt.

Here are the Papers that I'm currently reading/have read.

Elicit Machine Learning Guide: My current progress on the Elicit Machine Learning Reading List

Resources

[[LLM Fine Tuning Maven]] : A Maven Course on fine-tuning large language models

A Guide To Hyperparameters : These are some notes that I took about training hyper-parameters when fine-tuning models

Large Language Models : A quick introduction to what is a Large Language Model

Recommendation Systems : A quick dive into what a recommendation system is

Pytorch : A guide to using auto differentiation libraries in Machine Learning

[[Designing Machine Learning Systems]] : Some notes on Chip Huyen's Book

Machine Learning with PyTorch and Scikit-Learn : A guide on how to work with Pytorch for machine learning

[[RNN]] : A simplified implementation of RNNs in Pytorch

[[Retrieval Augmented Generation]] : How to do RAG

[[Chroma DB Chunking Guide]]

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