microsoft / AI-For-Beginners
A 12-week, 24-lesson beginner curriculum covering AI fundamentals, neural networks, computer vision, NLP, and AI ethics with TensorFlow and PyTorch labs.
microsoft / AI-For-Beginners
openai / CLIP
A neural network from OpenAI that scores how well text snippets match images, trained on image-text pairs for zero-shot prediction.
alphacep / vosk-api
Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
borglab / gtsam
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
spmallick / learnopencv
Learn OpenCV : C++ and Python Examples
AkashSingh3031 / The-Complete-FAANG-Preparation
Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you.
roboflow / notebooks
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
google / flax
Flax is a neural network library for JAX that is designed for flexibility.
Ramakm / AI-Engineering
A group of notebooks and other files which can help you learn AI from scratch.
IBM / aihwkit
IBM Analog Hardware Acceleration Kit
joanby / python-ml-course
Curso de Introducción a Machine Learning con Python
tensorflow / docs
TensorFlow documentation
skorch-dev / skorch
A scikit-learn compatible neural network library that wraps PyTorch
naganandy / graph-based-deep-learning-literature
links to conference publications in graph-based deep learning
nianticlabs / monodepth2
[ICCV 2019] Monocular depth estimation from a single image
tensorflow / probability
Probabilistic reasoning and statistical analysis in TensorFlow
gboeing / osmnx-examples
Gallery of OSMnx tutorials, usage examples, and feature demonstrations.
microsoft / CyberBattleSim
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
a-r-j / graphein
Protein Graph Library
huggingface / computer-vision-course
This repo is the homebase of a community driven course on Computer Vision with Neural Networks. Feel free to join us on the Hugging Face discord: hf.co/join/discord
wx-chevalier / AI-Notes
:books: [.md & .ipynb] Series of Artificial Intelligence & Deep Learning, including Mathematics Fundamentals, Python Practices, NLP Application, etc. 💫 人工智能与深度学习实战，数理统计篇 | 机器学习篇 | 深度学习篇 | 自然语言处理篇 | 工具实践 Scikit & Tensoflow & PyTorch 篇 | 行业应用 & 课程笔记
google / edward2
A simple probabilistic programming language.
erdogant / bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
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
hamza01055 / ml-from-scratch
Machine learning from scratch with Python and NumPy. Explore 12 chapters covering regression, neural networks, trees, clustering, SVMs, and autodiff with mathematical derivations, interactive notebooks, gradient verification, and 59 automated tests. No scikit-learn or PyTorch.
apple / corenet
CoreNet: A library for training deep neural networks
x4nth055 / pythoncode-tutorials
The Python Code Tutorials
SweiryDev / MicroCNN-TangNano20k
SystemVerilog SoC implementing MicroCNN accelerator on the Tang Nano 20K for BloodMNIST image classification.
karpathy / nn-zero-to-hero
A video course with Jupyter notebooks that builds neural networks and backpropagation from scratch, from basics to Transformer language models.
understandable-machine-intelligence-lab / Quantus
[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations