NVIDIA / torch-harmonics
Differentiable signal processing on the sphere for PyTorch
NVIDIA / torch-harmonics
khalil-research / PyEPO
A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
planetlabs / notebooks
interactive notebooks from Planet Engineering
blackinkkkxi / RAG_langchain
一个基于langchain实现RAG的简单示例
abhisheks008 / DL-Simplified
Deep Learning Simplified is an Open-source repository, containing beginner to advance level deep learning projects for the contributors, who are willing to start their journey in Deep Learning. Devfolio URL, https://devfolio.co/projects/deep-learning-simplified-f013
ncbi / datasets
NCBI Datasets is a new resource that lets you easily gather data from across NCBI databases.
Isi-dev / Google-Colab_Notebooks
A Collection of Google Colab Notebooks for various projects
deepset-ai / haystack-cookbook
👩🏻🍳 A collection of example notebooks using Haystack
superlinked / VectorHub
Deprecated historical repo. Superlinked now develops SIE, a self-hosted inference engine for embeddings, reranking, OCR, extraction, and document processing.
borglab / gtsfm
End-to-end SFM framework based on GTSAM
danielfrg / mkdocs-jupyter
Use Jupyter Notebook in mkdocs
zorost / AI-Engineering-Lab
A free, self-paced 24-week AI engineering course: Python, machine learning, LLMs, RAG, fine-tuning, agents and MCP, Azure and Vertex and Bedrock, and Databricks. 43 runnable notebooks, one continuous case study. MIT licensed, no signup. By Zorost Intelligence AI Lab.
MLO-lab / LeVJEPA
Efficient & Scalable Video Pretraining without the Heuristics
Nicolepcx / harness_engineering
This is the corresponding code for the O'Reilly book: Harness Engineering
TaroYamada08 / Hacker
This repository is maintained by Hex Core and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), AI security, vulnerability research, exploit development, reverse engineering, and more. 🔥 Also check: https://hackertraining.org
iam-veeramalla / RAG-crash-course
Learn RAG practically from absolute basics
mahmoodlab / CORAL
Spatial biology processing suite
AlexKrasnok / quantum-computing-lectures
Open quantum computing course for teaching, self-study, exam preparation, and quantum-technology career preparation.
NVDLI / fundamentals-of-deep-learning-zh
DLI Fundamentals of Deep Learning, Chinese Version
earlyaidopters / gumroad-resources
Auto-synced mirror of every free Early AI-dopters Gumroad resource. Newest first.
RichmondAlake / agent_harness_course
Technical companion to the O'Reilly Harness Engineering for AI Agents workshop
thinking0things / AlbertPro
Albert: a small quadruped that learns to walk with PPO in MuJoCo and runs the policy on an ESP32
mhmd-81 / mlops
just to learn
Jev-Engineering / cookbook
Sixty notebook recipes for typed decisions with Jev: Choice, Noul and Score patterns from a first classification to closed-loop control
norvig / pytudes
Python programs, usually short, of considerable difficulty, to perfect particular skills.
jpmorganchase / python-training
Python training for business analysts and traders
tensorflow / examples
TensorFlow examples
microsoft / TinyTroupe
LLM-powered multiagent persona simulation for imagination enhancement and business insights.
yandexdataschool / Practical_RL
A course in reinforcement learning in the wild
skorch-dev / skorch
A scikit-learn compatible neural network library that wraps PyTorch
realpython / materials
Bonus materials, exercises, and example projects for our Python tutorials
naganandy / graph-based-deep-learning-literature
links to conference publications in graph-based deep learning
makcedward / nlpaug
Data augmentation for NLP
bragai / bRAG-langchain
Everything you need to know to build your own RAG application
microsoft / PhiCookBook
This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks
matheusfacure / python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
BlockchainCommons / Learning-Bitcoin-from-the-Command-Line
A complete course for learning Bitcoin programming and usage from the command
scikit-learn-contrib / hdbscan
A high performance implementation of HDBSCAN clustering.
pinecone-io / examples
Jupyter Notebooks to help you get hands-on with Pinecone vector databases
keras-team / keras-io
Keras documentation, hosted live at keras.io
MARKTECHPOST-AI-MEDIA-INC / AI-Agents-Projects-Tutorials
Multi-agent systems, memory, planning, reasoning loops
huawei-noah / HEBO
Bayesian optimisation & Reinforcement Learning library developed by Huawei Noah's Ark Lab
huggingface / cookbook
Open-source AI cookbook
ipython / ipyparallel
IPython Parallel: Interactive Parallel Computing in Python
onnx / tensorflow-onnx
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
fengdu78 / machine_learning_beginner
机器学习初学者公众号作品
jamesturk / jellyfish
🪼 a python library for doing approximate and phonetic matching of strings.
aws-samples / amazon-bedrock-workshop
This is a workshop designed for Amazon Bedrock a foundational model service.
AllenDowney / ThinkBayes2
Text and code for the second edition of Think Bayes, by Allen Downey.
microsoft / CyberBattleSim
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
yandexdataschool / Practical_DL
DL course co-developed by YSDA, HSE and Skoltech
YBIFoundation / Internship
Jupyter Notebook
csinva / imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
nubank / fklearn
fklearn: Functional Machine Learning
aws-samples / amazon-bedrock-samples
This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models
microsoft / TextWorld
TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.
microsoft / KBLaM
Official Implementation of "KBLaM: Knowledge Base augmented Language Model"
INRIA / scikit-learn-mooc
Machine learning in Python with scikit-learn MOOC
shivammehta25 / Matcha-TTS
[ICASSP 2024] 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
CMA-ES / pycma
Python implementation of CMA-ES