caerang

caerang's Stars

• 170928/-Review-Playing-Atari-with-Deep-Reinforcement-Learning-Deep-Mind-Technologies

  [Review] Playing Atari with Deep Reinforcement Learning-Deep Mind Technologies

  Language:Python91

• AcornPublishing/python-oop-4e

  파이썬 객체지향 프로그래밍 4/e

  Language:Python43

• sarthakrastogi/quality-prompts

  Language:Python68943

• veritross/studiosr

  PyTorch library to accelerate super-resolution research

  Language:Python111

• 66RING/tiny-flash-attention

  flash attention tutorial written in python, triton, cuda, cutlass

  Language:Cuda19114

• cloneofsimo/minRF

  Minimal implementation of scalable rectified flow transformers, based on SD3's approach

  Language:Jupyter Notebook44032

• rolling-panda-san/notebooks

  Analysis on systematic trading strategies (e.g., trend-following, carry and mean-reversion). The result is regularly updated.

  Language:Jupyter Notebook47882

• clumsyhandyman/mad-from-scratch

  Machine learning, artificial intelligence, and data analytics built from scratch.

  Language:Python5017

• chulminkw/PerfectGuide

  Language:Jupyter Notebook116151

• viai957/llama-inference

  A simple implementation of Llama 1, 2. Llama Architecture built from scratch using PyTorch all the models are built from scratch that includes GQA (Grouped Query Attention) , RoPE (Rotary Positional Embeddings) , RMS Norm, FeedForward Block, Encoder (as this is only for Inferencing the model) , SwiGLU (Activation Function),

  Language:Python113

• faif/python-patterns

  A collection of design patterns/idioms in Python

  Language:Python40.4k6.9k

• mintisan/awesome-kan

  A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold Network field.

  2.5k229

• karpathy/llm.c

  LLM training in simple, raw C/CUDA

  Language:Cuda24.3k2.7k

• fg91/visualizing-cnn-feature-maps

  Visualizing CNN filters using PyTorch

  Language:Jupyter Notebook23469

• sjchoi86/yet-another-rl-tutorial

  Language:Jupyter Notebook6314

• dabeaz-course/practical-python

  Practical Python Programming (course by @dabeaz)

  Language:Python9.9k6.5k

• jiankaiwang/ioutracker

  IOUTracker implements a tracking algorithm or method to track objects based on their Intersection-Over-Union (IOU) information across the consecutive frames.

  Language:Python72

• ChengBinJin/Fast-Tracking-by-Detection

  This repository includes fast tracking by detection method based on SSD detector and multiple Kalman tracker.

  Language:Python325

• brennerm/PyTricks

  Collection of less popular features and tricks for the Python programming language

  Language:Python3.1k503

• uzi0espil/research-papers-implementation

  Implementing and summarizing interesting research papers in Machine Learning and Neural Network domain.

  Language:Jupyter Notebook178

• Seeed-Studio/node-red-contrib-ml

  Get started with AI vision at the edge with no coding experience at all!

  Language:JavaScript6530

• spmallick/learnopencv

  Learn OpenCV : C++ and Python Examples

  Language:Jupyter Notebook21.2k11.6k

• NVlabs/AL-MDN

  Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

  Language:Python16723

• coderonion/awesome-yolo-object-detection

  🚀🚀🚀 A collection of some awesome public YOLO object detection series projects.

  1.2k189

• scutan90/CNN-Visualization

  CNN可视化、理解CNN

  672156

• christianversloot/machine-learning-articles

  🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.

  3.4k722

• boostcampaitech2/final-project-level3-cv-18

  [신발 Tag 상품번호 인식기​] OCR 을 이용하여 신발 태그의 품번을 인식하여 사용자에게 신발 정보를 제공하는 서비스를 기획 및 구현하였습니다.

  Language:Jupyter Notebook115

• lucidrains/denoising-diffusion-pytorch

  Implementation of Denoising Diffusion Probabilistic Model in Pytorch

  Language:Python8.3k1k

• rafaelpadilla/review_object_detection_metrics

  Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.

  Language:Python1.1k215

• trekhleb/machine-learning-experiments

  🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

  Language:Jupyter Notebook1.6k299