Selection — 3 881

A collection of image segmentation algorithms based on diffusion methods.

SciPy tutorials. This is outdated, check out scipy-lecture-notes.

A recommendation engine library for Python.

Bayesian Inference Tools in Python.

Series of notebooks for learning scikit-learn.

Tweets Sentiment Analyzer

Sentiment classifier using word sense disambiguation.

Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.

Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run Cabocha(ISO--8859-1 configured) in Python.

IPython notebooks for EEG/MEG data processing using mne-python.

IPython notebooks for a complete course around understanding Nervana's Neon.

Recipes for using Python's pandas library.

Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.

Code for Data Science at Olin College, Spring 2014.

Code for Allen Downey's book Think Complexity.

Text and supporting code for Think OS: A Brief Introduction to Operating Systems.

"I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself."

TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.

Machine Learning for RC Cars.

IPython notebooks from Data School's video tutorials on scikit-learn.

Notebooks and code for the book "Introduction to Machine Learning with Python"

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

Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Build tool for data science pipelines.

This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.

Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.

Given clinical parameters about a patient, can we predict whether or not they have heart disease?

This basically to gauge the understanding of Machine Learning Workflow and Regression technique in specific.

An easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.

A simple Python framework for building ML/DL workflows and LLM agents faster, with prebuilt trainers, agent templates, logging, configuration management, and much more.

Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.

nnbuilder is a python package that lets you build neural networks in 1 line

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. [Deprecated]

Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.

TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs.