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📚 Freely available programming books
A curated list of awesome Machine Learning frameworks, libraries and software.
scikit-learn: machine learning in Python
Scrapy, a fast high-level web crawling & scraping framework for Python.
A toolkit for developing and comparing reinforcement learning algorithms.
PyTorch Tutorial for Deep Learning Researchers
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials,…
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
The open source developer platform to build AI agents and models with confidence. Enhance your AI applications with end-to-end tracking, observability, and evaluations, all in one integrated platform.
The official Python SDK for Model Context Protocol servers and clients
PyTorch implementations of Generative Adversarial Networks.
Jupyter metapackage for installation and documentation
Image augmentation for machine learning experiments.
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Tra…
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
Cracking the Coding Interview 6th Ed. Python Solutions
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Hummingbird compiles trained ML models into tensor computation for faster inference.
Python library for loading and using triangular meshes.
Code for the paper "Evaluating Large Language Models Trained on Code"
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds