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ilbaks's Projects

aws-mlu-explain icon aws-mlu-explain

Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/

cookiecutter-data-science icon cookiecutter-data-science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

cookiecutter-machine-learning icon cookiecutter-machine-learning

Enhanced cookiecutter template for Data Science projects. Based on @drivendata's practices, this version offers additional features for an efficient and streamlined workflow.

d2l-en icon d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

dcai-lab icon dcai-lab

Lab assignments for Introduction to Data-Centric AI, MIT IAP 2023 ๐Ÿ‘ฉ๐Ÿฝโ€๐Ÿ’ป

fastapi_course icon fastapi_course

ะ’ะฒะพะดะฝั‹ะน ะบัƒั€ั ะฟะพ ั„ั€ะตะนะผะฒะพั€ะบัƒ FastAPI ะฝะฐ YouTube

former icon former

Simple transformer implementation from scratch in pytorch.

freeplane_markdownhelper icon freeplane_markdownhelper

Markdown HelperAddOn for Freeplane is a tool to create markdown documents in a Freeplane mindmapping way. The general idea of this AddOn is to insert special nodes in your map that help to translate the information in a mindmap into a Markdown document. You obtain a good looking document retaining the flexibility that offers Freeplane and mindmappi

gpt-summarizer icon gpt-summarizer

A Python script that utilizes OpenAI's GPT-3.5-turbo model to process and summarize long-form text files.

hands-on-llms icon hands-on-llms

๐Ÿฆ– ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป about ๐—Ÿ๐—Ÿ๐— ๐˜€, ๐—Ÿ๐—Ÿ๐— ๐—ข๐—ฝ๐˜€, and ๐˜ƒ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ ๐——๐—•๐˜€ for free by designing, training, and deploying a real-time financial advisor LLM system ~ ๐˜ด๐˜ฐ๐˜ถ๐˜ณ๐˜ค๐˜ฆ ๐˜ค๐˜ฐ๐˜ฅ๐˜ฆ + ๐˜ท๐˜ช๐˜ฅ๐˜ฆ๐˜ฐ & ๐˜ณ๐˜ฆ๐˜ข๐˜ฅ๐˜ช๐˜ฏ๐˜จ ๐˜ฎ๐˜ข๐˜ต๐˜ฆ๐˜ณ๐˜ช๐˜ข๐˜ญ๐˜ด

haystack icon haystack

:mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

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