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Practical course about Large Language Models.
🧮 A collection of resources to learn mathematics for machine learning
In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
Google AI 2018 BERT pytorch implementation
A paper list of some recent Transformer-based CV works.
📡 All You Need to Know About Deep Learning - A kick-starter
YSDA course in Natural Language Processing
Papers from the computer science community to read and discuss.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
DSA notes of Dr Naveen Garg, IIT Delhi, NPTEL
Hackers' Guide to Language Models
Course notes for CS224N Winter17
This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearn…
This repository contains links to machine learning exams, homework assignments, and exercises that can help you test your understanding.
Code Transformer neural network components piece by piece
ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many…
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
A curated list of awesome Machine Learning frameworks, libraries and software.
VIP cheatsheets for Stanford's CS 229 Machine Learning
All the available resources to master MLOPS from scratch
🛠 MLOps end-to-end guide and tutorial website, using IBM Watson, DVC, CML, Terraform, Github Actions and more.
Exercises and supplementary material for the machine learning operations course at DTU.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Learn how to design, develop, deploy and iterate on production-grade ML applications.