thisisamish

thisisamish's Stars

• sveltejs/svelte

  web development for the rest of us

  Language:JavaScript79.7k8557.4k4.2k

• zed-industries/zed

  Code at the speed of thought – Zed is a high-performance, multiplayer code editor from the creators of Atom and Tree-sitter.

  Language:Rust49.2k2119k3k

• karpathy/nanoGPT

  The simplest, fastest repository for training/finetuning medium-sized GPTs.

  Language:Python37.1k3713175.9k

• wesm/pydata-book

  Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

  Language:Jupyter Notebook22.2k1.5k12315.2k

• karpathy/minGPT

  A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

  Language:Python20.1k257722.5k

• ankitects/anki

  Anki's shared backend and web components, and the Qt frontend

  Language:Rust18.8k3439622.1k

• Netflix/conductor

  Conductor is a microservices orchestration engine.

  Language:Java12.8k5501.6k2.3k

• karpathy/micrograd

  A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

  Language:Jupyter Notebook10.3k149301.5k

• NielsRogge/Transformers-Tutorials

  This repository contains demos I made with the Transformers library by HuggingFace.

  Language:Jupyter Notebook9.4k1384511.4k

• lukeed/clsx

  A tiny (239B) utility for constructing `className` strings conditionally.

  Language:JavaScript8.3k1756143

• xitanggg/open-resume

  OpenResume is a powerful open-source resume builder and resume parser. https://open-resume.com/

  Language:TypeScript6.7k3392652

• typehero/typehero

  Connect, collaborate, and grow with a community of TypeScript developers

  Language:TypeScript5.9k17502419

• pymupdf/PyMuPDF

  PyMuPDF is a high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.

  Language:Python5.4k642k511

• huntabyte/shadcn-svelte

  shadcn/ui, but for Svelte. ✨

  Language:Svelte5.3k47602331

• serverless-nextjs/serverless-next.js

  ⚡ Deploy your Next.js apps on AWS Lambda@Edge via Serverless Components

  Language:TypeScript4.5k29924457

• karpathy/makemore

  An autoregressive character-level language model for making more things

  Language:Python2.6k338673

• fastai/course22

  The fast.ai course notebooks

  Language:Jupyter Notebook2.5k5072971

• google-deepmind/gemma

  Open weights LLM from Google DeepMind.

  Language:Python2.4k3334308

• rauchg/how-is-this-not-illegal

  A demo of using RSC and Vercel Postgres, legally

  Language:JavaScript6114323

• t3dotgg/chirp

  Language:TypeScript39441661

• thorfdbg/libjpeg

  A complete implementation of 10918-1 (JPEG) coming from jpeg.org (the ISO group) with extensions for HDR, lossless and alpha channel coding standardized as ISO/IEC 18477 (JPEG XT).

  Language:C++323218480

• rslim087a/Java-Bootcamp-Resources

  Language:Java217173279

• WebDevSimplified/youtube-video-player-clone

  Language:JavaScript20746127

• dadoonet/legacy-search

  Demo project showing how to add elasticsearch to a legacy application.

  Language:Java111127844

• dschoon/react-waves

  React component wrapper for wavesurfer.js

  Language:JavaScript7344321

• AmanSavaria1402/TableNet

  TableNet: Deep Learning model for end-to-end Table Detection and Tabular data extraction from Scanned Data Images In modern times, more and more number of people are sharing their documents as photos taken from smartphones. A lot of these documents contain lots of information in one or more tables. These tables often contain very important information and extracting this information from the image is a task of utmost importance. In modern times, information extraction from these tables is done manually, which requires a lot of effort and time and hence is very inefficient. Therefore, having an end-to-end system that given only the document image, can recognize and localize the tabular region and also recognizing the table structure (columns) and then extract the textual information from the tabular region automatically will be of great help since it will make our work easier and much faster. TableNet is just that. It is an end-to-end deep learning model that can localize the tabular region in a document image, understand the table structure and extract text data from it given only the document image. Earlier state-of-the-art deep learning methods took the two problems, that is, table detection and table structure recognition (recognizing rows and columns in the table) as separate and treated them separately. However, given the interdependence of the two tasks, TableNet considers them as two related sub-problems and solves them using a single neural network. Thus, also making it relatively lightweight and less compute intensive solution.

  Language:Jupyter Notebook45327

• mVirtuoso21/JPEG-Image-Compressor

  A Python program that compresses raw images based on the JPEG compression algorithm.

  Language:Python39126

• SolbiatiAlessandro/google_myactivity_scraper

  scraper for myactivity.google.com

  Language:Python10303

• AshishSalaskar1/TableNet_Implementation

  Extract Tabular data from scanned document images and save the tabular data into CSV files. Used a Encoder-Decoder based architecture for building the model

  Language:Jupyter Notebook9244

• srinit16/newgen

  Language:Java1101