Machine Learning
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A tutorial series comparing how to implement data science concepts and build applications in both Python and R ecosystems.
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
A collection of TensorFlow practice exercises covering fundamental machine learning concepts from linear regression to CNNs.
A blockchain framework for hosting and collaboratively training publicly available machine learning models with free predictions.
A JAX library for nonlinear optimization including root finding, minimization, fixed points, and least squares.
A production-ready FastAPI skeleton app for serving machine learning models with built-in authentication and testing.
TensorFlow implementation of GAN-CLS algorithm for generating images from text descriptions using adversarial networks.
A deep learning system that classifies food images into 230 categories and retrieves matching recipes using convolutional neural networks.
A curated collection of open-source machine learning models compatible with Apple's Core ML framework.
A Swift library providing NumPy-like matrix operations and machine learning algorithms for iOS/macOS development.
A quantization extension for Keras that provides drop-in replacement layers for creating quantized deep learning models in TensorFlow.
A Python framework for gradient-free optimization, featuring common algorithms like genetic algorithms and simulated annealing.
A Python library providing evaluation metrics and diagnostic tools for recommender systems.
A simplified Keras-like framework for PyTorch that reduces boilerplate code for training neural networks.
An optimized distributed gradient boosting library for fast and accurate machine learning on large datasets.
A Python library for Bayesian inference in Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs) with nonparametric extensions.
TensorFlow implementation of R-Net for machine reading comprehension on the SQuAD dataset.
A vector space search engine, vector database, and key/value store for efficient string processing and vector operations.
A collection of IPython notebooks containing machine learning experiments and examples using scikit-learn and related Python libraries.
A large-scale StarCraft: Brood War replay dataset for AI research, containing 65,646 games with frame and action data.
A Python library for generating high-quality synthetic tabular data using GANs, diffusion models, and large language models.
A TensorFlow-based image recognition system for captchas that works without image segmentation.
A JupyterLab extension that breaks the linear presentation of notebooks by enabling sticky, floating cells for interactive dashboards.
A Go library implementing feed-forward and Elman recurrent neural networks for machine learning tasks.
Experimental implementations of financial machine learning techniques from 'Advances in Financial Machine Learning' for stochastic time series data.
A junction tree variational autoencoder for generating valid molecular graphs with desired chemical properties.
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
A command-line tool for neural network inference using Unix pipeline philosophy.
A Julia implementation of the scikit-learn API, providing a uniform interface for machine learning models from both Julia and Python ecosystems.
Open-source teaching materials for a practical Machine Learning in Finance course, focusing on industry tools and real-world use cases.
Autograd automatically differentiates native Torch code, enabling automatic gradient computation for machine learning models.
A Go library implementing feedforward/backpropagation neural networks with support for multiple activation functions, solvers, and classification modes.
A JAX-based machine learning framework for configuring and training large-scale models with high efficiency on TPUs and GPUs.
A web application that uses a CNN model to recognize handwritten Chinese characters from an online drawing canvas.
A Scala toolkit for deployable probabilistic modeling using imperatively-defined factor graphs.
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