yashika-git
Follow
Starred repositories
70
stars
written in Jupyter Notebook
Clear filter
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
A collection of various deep learning architectures, models, and tips
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…
llama3 implementation one matrix multiplication at a time
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
This repository contains demos I made with the Transformers library by HuggingFace.
Python toolkit for quantitative finance
Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Jupyter notebooks for the Natural Language Processing with Transformers book
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
Solve puzzles. Improve your pytorch.
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collec…
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackable.
Recipes for shrinking, optimizing, customizing cutting edge vision models. 💜
Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻🦸🏽
What would you do with 1000 H100s...
Lightweight, useful implementation of conformal prediction on real data.
Generative Adversarial Networks implemented in PyTorch and Tensorflow
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-…
Awesome Machine Unlearning (A Survey of Machine Unlearning)
