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🦜🔗 Build context-aware reasoning applications
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
This repository contains implementations and illustrative code to accompany DeepMind publications
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
PRML algorithms implemented in Python
Code release for NeRF (Neural Radiance Fields)
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑🔬
Deep Learning Specialization by Andrew Ng on Coursera.
Visualizations for machine learning datasets
OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image genera…
COCO API - Dataset @ http://cocodataset.org/
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
Repo for the Deep Learning Nanodegree Foundations program.
A simplified implemention of Faster R-CNN that replicate performance from origin paper
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.
[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models
Code for the Lovász-Softmax loss (CVPR 2018)
Tensorflow实战学习笔记、代码、机器学习进阶系列
a tensorflow implementation of WGAN
[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS). It is also a PyTorch implementation of the NeurIPS 2…
We extend Segment Anything to 3D perception by combining it with VoxelNeXt.
categorical variational autoencoder using the Gumbel-Softmax estimator
Tutorial for using Kitti dataset easily