A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.
A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.
A canon of deep learning optimization algorithms.
Adaptive Neural Execution Engine for transformers. Per-token sparse inference with dynamic layer skipping, profiler-based gating, and KV-cache-safe compute reduction.
PyTorch library for creating and training sequence autoencoders in just two lines of code
TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs.
Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. [Deprecated]
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
nnbuilder is a python package that lets you build neural networks in 1 line
Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.
A simple Python framework for building ML/DL workflows and LLM agents faster, with prebuilt trainers, agent templates, logging, configuration management, and much more.
An easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.
This basically to gauge the understanding of Machine Learning Workflow and Regression technique in specific.
Given clinical parameters about a patient, can we predict whether or not they have heart disease?
Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.
This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.
Build tool for data science pipelines.
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Notebooks and code for the book "Introduction to Machine Learning with Python"
IPython notebooks from Data School's video tutorials on scikit-learn.
Machine Learning for RC Cars.
TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.
"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."
Text and supporting code for Think OS: A Brief Introduction to Operating Systems.
Code for Allen Downey's book Think Complexity.
Code for Data Science at Olin College, Spring 2014.
Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.
Recipes for using Python's pandas library.
IPython notebooks for a complete course around understanding Nervana's Neon.
IPython notebooks for EEG/MEG data processing using mne-python.
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.
Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.
Sentiment classifier using word sense disambiguation.
Tweets Sentiment Analyzer
Series of notebooks for learning scikit-learn.
Bayesian Inference Tools in Python.
A recommendation engine library for Python.
SciPy tutorials. This is outdated, check out scipy-lecture-notes.
A collection of image segmentation algorithms based on diffusion methods.
Sarah Palin LDA - Topic Modelling the Sarah Palin emails.
data-science-ipython-notebooks - Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
A gallery of interesting IPython notebooks
numpic
thinking stats 2
Interactive SVM Explorer, using Dash and scikit-learn
Fundamentals of machine learning in python.