500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

bigdata-and-intelligence-analytics

DAMG assignment 1

busines_meeting_summarization

codeformer

[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

deep-reinforcement

Deep Reinforcement Learning Class.

diffusion-models-class

Materials for the Hugging Face Diffusion Models Course

fastapi-heroku

Deploying ML model with fastapi on heroku

forecasting-global-tempeartures

Forecasting global temparatures based on multiple factors influencing them

industry-machine-learning

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

llama-from-scratch

Training and running metas llama from scratch

llava

[NeurIPS'23 Oral] Visual Instruction Tuning: LLaVA (Large Language-and-Vision Assistant) built towards GPT-4V level capabilities.

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

llms-from-scratch

Implementing a ChatGPT-like LLM from scratch, step by step

lwm

Large world model capable of parallelization across multiple GPUS and have 1M context length

ml-for-beginners

12 weeks, 24 lessons, classic Machine Learning for all

ml-notebooks

:fire: A series of code examples for all sorts of machine learning tasks and applications.

ml-papers-explained

Explanation to key concepts in ML

ml-roadmap-for-2022

A curated list of Machine learning videos, links, projects and datasets to help you conquer the ML landscape in 6 months

ner-re-with-transformers

Building NER and RE components using HuggingFace Transformers

padl

prasanth-dwadasi

Config files for my GitHub profile.

skills-copilot-codespaces-vscode

My clone repository

skunks_skool

Skunks Skool Tutorials

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.