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Easy to use stem (e.g. instrumental/vocals) separation from CLI or as a python package, using a variety of amazing pre-trained models (primarily from UVR)
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
A curated list of awesome ETL frameworks, libraries, and software.
🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.
System design patterns for machine learning
A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Statistical Rethinking Course for Jan-Mar 2023
Source code for Algorithmic Trading with Python (2020) by Chris Conlan
Summaries and resources for Designing Machine Learning Systems book (Chip Huyen, O'Reilly 2022)
All Algorithms implemented in Python
Cracking the Coding Interview 6th Ed. Python Solutions
JSON conversion and parsing for VBA
A repository of 60 useful data science prompts for ChatGPT
AI PDF chatbot agent built with LangChain & LangGraph
Natural Gradient Boosting for Probabilistic Prediction
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
Guides and lab assets for the workshop "Survey of Advanced Computing in Azure"
Live demo using Angular, github.dev, codespaces, copilot, azure static web apps, and devcontainers
This is a repository for Microsoft Power Automate, Power Apps, and Azure Logic Apps connectors
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Lab 1 and Lab 2 for the Azure Machine Learning Workshop
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
A privacy-first, open-source platform for knowledge management and collaboration. Download link: http://github.com/logseq/logseq/releases. roadmap: https://logseq.io/p/NX4mc_ggEV