A complete guide to start and improve your LLM skills in 2025 with little background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

Code for the best paper at ISIC Skin Image Analysis Workshop paper at CVPR 2020.

Modern implementation of the hybrid genetic search (HGS) algorithm specialized to the capacitated vehicle routing problem (CVRP). This code also includes an additional neighborhood called SWAP*.

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

PyCrown - Fast raster-based individual tree segmentation for LiDAR data

YOLOXをGoogle Colaboratory上で訓練しONNX、TensorFlow-Lite形式のファイルをエクスポートするサンプル(This is a sample to train YOLOX on Google Colaboratory and export a file in ONNX format)

Techniques for deep learning with satellite & aerial imagery

Tensorflow Advanced Technique Specialization

OpenMMLab Rotated Object Detection Toolbox and Benchmark

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and l…

Material for the SciPy 2017 Cython tutorial

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Update PDEKoopman code to Tensorflow 2

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

Caffe: a fast open framework for deep learning.

NYU Deep Learning Spring 2020

A curated collection of free learning resources on topics related to Machine Learning.

Simulação Estocástica

Introduction to Mathematical Computing with Python and Jupyter

Python-based instrumentation library from the Mabuchi Lab.

Source code for 'Dynamical Systems with Applications Using Python' by Stephen Lynch

Learn OpenCV : C++ and Python Examples

Jupyter notebooks for "A high-bias, low-variance introduction to Machine Learning for physicists"

Production Data Science: a workflow for collaborative data science aimed at production