haynaou

haynaou's Stars

• donnemartin/system-design-primer

  Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

  Language:Python269k6.6k31745.4k

• astral-sh/ruff

  An extremely fast Python linter and code formatter, written in Rust.

  Language:Rust30.7k765.1k1k

• rasbt/LLMs-from-scratch

  Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step

  Language:Jupyter Notebook25.6k285792.8k

• roboflow/supervision

  We write your reusable computer vision tools. 💜

  Language:Python18.5k1294081.4k

• state-spaces/mamba

  Mamba SSM architecture

  Language:Python12.4k1024911k

• eza-community/eza

  A modern alternative to ls

  Language:Rust10.9k23418193

• stas00/ml-engineering

  Machine Learning Engineering Open Book

  Language:Python10.6k11021641

• karpathy/minbpe

  Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

  Language:Python9k8336826

• aishwaryanr/awesome-generative-ai-guide

  A one stop repository for generative AI research updates, interview resources, notebooks and much more!

  7.2k24381.5k

• LargeWorldModel/LWM

  Language:Python7.1k6670547

• reorproject/reor

  Private & local AI personal knowledge management app.

  Language:TypeScript6.8k42168410

• OpenDriveLab/UniAD

  [CVPR 2023 Best Paper Award] Planning-oriented Autonomous Driving

  Language:Python3.3k35175363

• sgrvinod/a-PyTorch-Tutorial-to-Object-Detection

  SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection

  Language:Python3k4793715

• BolajiAyodeji/awesome-technical-writing

  :books: A curated list of awesome resources: articles, books, videos, tools, podcasts about technical writing.

  1.7k512297

• JifuZhao/DS-Take-Home

  My solution to the book A Collection of Data Science Take-Home Challenges

  Language:Jupyter Notebook1.7k391861

• WassimBenzarti/colab-ssh

  Connect to Google Colab using SSH

  Language:Python9771566127

• raspberrypi/picamera2

  New libcamera based python library

  Language:Python82434610176

• NVlabs/FB-BEV

  Official PyTorch implementation of FB-BEV & FB-OCC - Forward-backward view transformation for vision-centric autonomous driving perception

  Language:Python632304443

• OpenDriveLab/OpenLane-V2

  [NeurIPS 2023 Track Datasets and Benchmarks] OpenLane-V2: The First Perception and Reasoning Benchmark for Road Driving

  Language:Jupyter Notebook5352110061

• markriedl/transformer-walkthrough

  A walkthrough of transformer architecture code

  Language:Jupyter Notebook2948433

• sea-bass/pyrobosim

  ROS 2 enabled 2D mobile robot simulator for behavior prototyping.

  Language:Python217138536

• Eaphan/GLENet

  GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [IJCV2023]

  Language:Python18051911

• younader/Vesuvius-First-Letters

  Language:Python1315220

• robot-motion/bench-mr

  Motion Planning Benchmark

  Language:Jupyter Notebook7883534

• seddonym/impulse

  Impulse is a command line tool for exploring the imports in a Python package.

  Language:Python35483

• jeremyfix/deeplearning-lectures

  Deep learning lecture materials

  Language:Python3041713

• thetarnav/streaming-markdown

  Streaming markdown à la ChatGPT (WIP)

  Language:JavaScript23310

• allenye66/Deep-Learning-Autonomous-Drones

  Here is a conglomeration of file depcting the code we wrote to create an autonomous drone using a CNN-LSTM model to aid in food and package delivery during the 2020 quarantine. Steering Angle Dataset Exploration: Here is where we explored methods in making our CNN-LSTM predictor, as well as coded the final version. We also have graphs for the results of our code. We also define the Gaussian and Edge detection preprocessing functions over here. Yolov3 Bounding Boxes: Here is where we created a transfer learning model from the Yolov3 architecture to find bounding boxes of cars, people, and trees in our images. These bounding boxes were used by our probability model to calculate the probability of collision. Weight determination functions: Here is where we defined the functions user to calculate the probability of colliding into any given object. The final probability determination function can be found in the Yolov3 script, as well as the UserModelLibrary scripts. Data Exploration: Here is where we explored the data intially given to us, and found that the data was abnormally distributed. This helped us deermine the wraparound problem, as well as why our models prediction were near 0 in the early stages of the process. Trial.py: This is the script to fly the actualy drone. UserModelLibraries: This is the final conglomeration of all of our code - the probability functions, models, and pre/post processing function used to run our algorithms. All pictures and graphs are also included in the pictures and graphs photo.

  Language:Jupyter Notebook91

• shadanan/codeanim

  Tool to animate Visual Studio Code for presentation.

  Language:Python5201

• shadanan/cipherly

  App for sharing secrets.

  Language:Svelte4331