Search Results for "machine learning python" - Page 2
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Showing 171 open source projects for "machine learning python"
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Behaviour Suite Reinforcement Learning
bsuite is a collection of carefully-designed experiments
bsuite is a research framework developed by Google DeepMind that provides a comprehensive collection of experiments for evaluating the core capabilities of reinforcement learning (RL) agents. Its main goal is to identify, measure, and analyze fundamental aspects of learning efficiency and generalization in RL algorithms. The library enables researchers to benchmark their agents on standardized tasks, facilitating reproducible and transparent comparisons across different approaches. Each... -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual... -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. ... -
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Google Cloud Platform Python Samples
Code samples used on cloud.google
Google Cloud Platform Python Samples repository is a large, curated collection of Python code examples that demonstrate how to use a wide range of Google Cloud services in real-world scenarios. It serves as a practical companion to official documentation, providing runnable snippets that illustrate how to authenticate, configure environments, and interact with APIs across products such as storage, AI services, and data processing tools. -
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AIMET
AIMET is a library that provides advanced quantization and compression
...For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed. -
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RecBole
A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library. We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. RecBole is developed based on Python and PyTorch for... -
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Public APIs
A collective list of free APIs
...Curated by community contributors and the team at APILayer, it serves as a centralized resource for discovering APIs across a wide range of domains, including data, machine learning, weather, entertainment, and finance. The project aims to make API exploration and integration more accessible by offering a single, organized index of open and free-to-use APIs. Developers can leverage this list to enhance their products, prototypes, or research projects without the need to build data sources from scratch. ... -
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Professional Services
Common solutions and tools developed by Google Cloud
Professional Services repository is a collection of real-world solutions, tools, and reference implementations developed by Google Cloud’s Professional Services team to address common enterprise challenges. Unlike simple sample repositories, it focuses on production-oriented use cases such as data pipelines, machine learning workflows, infrastructure automation, and security management. The repository contains a wide variety of projects, including tools for validating data migrations, generating large datasets for testing, building analytics dashboards, and automating policy enforcement in cloud environments. These solutions are intended to serve as blueprints that organizations can adapt and extend for their own needs rather than turnkey products. ... -
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BayesianOptimization
A Python implementation of global optimization with gaussian processes
BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes. -
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Opacus
Training PyTorch models with differential privacy
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source,... -
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PyOpenCL
OpenCL integration for Python, plus shiny features
PyOpenCL is a Python wrapper for the OpenCL framework, providing seamless access to parallel computing on CPUs, GPUs, and other accelerators. It enables developers to harness the full power of heterogeneous computing directly from Python, combining Python’s ease of use with the performance benefits of OpenCL. PyOpenCL also includes convenient features for managing memory, compiling kernels, and interfacing with NumPy, making it a preferred choice in scientific computing, data analysis, and machine learning workflows that demand acceleration. -
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Tile Kernels
A kernel library written in tilelang
Tile Kernels is a DeepSeek kernel library written with TileLang for high-performance AI and machine-learning workloads. It contains specialized kernels for areas such as mixture-of-experts routing, quantization, batched transpose operations, Engram gating, and Manifold HyperConnection components. The project includes both optimized kernel implementations and PyTorch reference versions for comparison and validation. It is aimed at developers and researchers who work close to model internals and need efficient low-level building blocks. ... -
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Otter-Grader
A Python and R autograding solution
...It is designed to work with classes at any scale by abstracting away the autograding internals in a way that is compatible with any instructor's assignment distribution and collection pipeline. Otter supports local grading through parallel Docker containers, grading using the autograder platforms of 3rd party learning management systems (LMSs), the deployment of an Otter-managed grading virtual machine, and a client package that allows students to run public checks on their own machines. Otter is designed to grade Python scripts and Jupyter Notebooks, and is compatible with a few different LMSs, including Canvas and Gradescope. -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before... -
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kagglehub
Python library to access Kaggle resources
...It is useful for machine learning workflows where data, models, and notebook artifacts need to be pulled into scripts, experiments, or pipelines. kagglehub also supports authentication so users can access private or restricted resources when their account has permission. Its main value is making Kaggle assets easier to consume programmatically in Python-first data science and AI development workflows. -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations, molecular dynamics, spin-state energetics, and surface catalysis workflows with the same pretrained network by switching a task flag. ... -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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Kornia
Open Source Differentiable Computer Vision Library
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within... -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
Writer Framework is an open source platform designed to help developers build AI-powered applications by combining a visual interface builder with a Python-based backend architecture. It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large language models, knowledge graphs, and custom machine learning workflows into user-facing applications. ... -
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. ... -
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Tree
tree is a library for working with nested data structures
Tree (dm-tree) is a lightweight Python library developed by Google DeepMind for manipulating nested data structures (also called pytrees). It generalizes Python’s built-in map function to operate over arbitrarily nested collections — including lists, tuples, dicts, and custom container types — while preserving their structure. This makes it particularly useful in machine learning pipelines and JAX-based workflows, where complex parameter trees or hierarchical state representations are common. ... -
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LLM CLI
Access large language models from the command-line
A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
Jupyter Docker Stacks provides a curated set of ready-to-run Docker container images that bundle Jupyter applications with popular data science and computing tools, enabling users to quickly start working in a reproducible environment. These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run consistently across machines. Users can pull a particular stack image and launch a Jupyter server without worrying about installing Python, R, or complex dependencies themselves — everything needed is baked into the container. This makes the stacks especially useful for education, demos, collaborative coding, and CI/CD workflows where consistent environments are crucial, and it integrates smoothly with cloud platforms, JupyterHub deployments, and Binder for interactive sharing. -
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Hypothesis
The property-based testing library for Python
Hypothesis is a powerful library for property-based testing in Python. Instead of writing specific test cases, users define properties and Hypothesis generates random inputs to uncover edge cases and bugs. It integrates with unittest and pytest, shrinking failing examples to minimal reproducible cases. Widely adopted in production systems, Hypothesis boosts code reliability by exploring input spaces far beyond manually crafted tests. -
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The Data Engineering Handbook
Links to everything you'd ever want to learn about data engineering
The Data Engineering Handbook is a comprehensive, community-curated repository that aggregates essential learning resources for anyone interested in becoming a professional data engineer. Rather than being a code project itself, it’s a learning handbook that links to books, articles, tutorials, community groups, boot camps, and real-world project examples that collectively form a roadmap to mastering data engineering skills. It includes beginner and intermediate boot camps, interview guides,...
