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Amit Parashar's Projects

awesome-rust-machinelearning icon awesome-rust-machinelearning

This repository is a list of machine learning libraries written in Rust. It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers, etc. 🦀

aws-devops-zero-to-hero icon aws-devops-zero-to-hero

AWS zero to hero repo for devops engineers to learn AWS in 30 Days. This repo includes projects, presentations, interview questions and real time examples.

concurrent-camera-reader icon concurrent-camera-reader

The code repo for Youtube tutorial series about using Python asyncio with OpenCV to grab frames from video cameras concurrently

coursera-deep-learning-specialization icon coursera-deep-learning-specialization

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks;

examples icon examples

A repository to host extended examples and tutorials

get-things-done-with-prompt-engineering-and-langchain icon get-things-done-with-prompt-engineering-and-langchain

LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.

kubernetes-gpu-guide icon kubernetes-gpu-guide

This guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster.

ml-road icon ml-road

Machine Learning Resources, Practice and Research

system-design icon system-design

Learn how to design systems at scale and prepare for system design interviews

tika icon tika

The Apache Tika toolkit detects and extracts metadata and text from over a thousand different file types (such as PPT, XLS, and PDF).

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