Search Results for "machine learning python" - Page 19
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Showing 3051 open source projects for "machine learning python"
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Fuzzy machine learning framework
A library and a GUI front-end for fuzzy machine learning
Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC or SQLite; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use. -
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GraphEmbedding
Implementation and experiments of graph embedding algorithms
...Example scripts demonstrate how to train models and retrieve embeddings for downstream graph analysis or machine learning tasks. -
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AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets, engineering predictive features, building models with AutoML, connecting to SQL databases, and producing visual outputs — all driven by natural language or programmatic instructions. ... -
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NVIDIA Isaac Lab
Unified framework for robot learning built on NVIDIA Isaac Sim
Isaac Lab is an open-source modular robotics learning framework built atop Isaac Sim. It simplifies research workflows across reinforcement learning, imitation learning, and motion planning by offering robust, GPU-accelerated simulation with realistic sensor and physics fidelity—ideal for sim-to-real robot training. Compatible and optimized for use with Isaac Sim versions (e.g., Sim 5.0 and 4.5). GPU-accelerated, high-fidelity physics and sensor simulation suitable for complex learning... -
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Top Deep Learning Projects
A list of popular github projects related to deep learning
TopDeepLearning is a curated index of the most popular GitHub projects related to deep learning, ranked by their star count. Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly... -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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Appfl
Advanced Privacy-Preserving Federated Learning framework
APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups. -
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UCCL
UCCL is an efficient communication library for GPUs
UCCL is a high-performance GPU communication library designed to support distributed machine learning workloads and large-scale AI systems. The library focuses on enabling efficient data transfer and collective communication between GPUs during training and inference processes. It supports a variety of communication patterns including collective operations such as all-reduce as well as peer-to-peer transfers that are commonly used in modern machine learning architectures. ... -
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Codon
A high-performance, zero-overhead, extensible Python compiler
Codon is a high-performance Python compiler that compiles Python code to native machine code without any runtime overhead. Typical speedups over Python are on the order of 100x or more, on a single thread. Codon supports native multithreading which can lead to speedups many times higher still. The Codon framework is fully modular and extensible, allowing for the seamless integration of new modules, compiler optimizations, domain-specific languages and so on. ... -
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Turing.jl
Bayesian inference with probabilistic programming
Bayesian inference with probabilistic programming. -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,... -
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Open Notebook
An Open Source implementation of Notebook LM with more flexibility
Open Notebook is an open-source, privacy-focused alternative to Google’s Notebook LM that gives users full control over their research and AI workflows. Designed to be self-hosted, it ensures complete data sovereignty by keeping your content local or within your own infrastructure. The platform supports 16+ AI providers—including OpenAI, Anthropic, Ollama, Google, and LM Studio—allowing flexible model choice and cost optimization. Open Notebook enables users to organize and analyze... -
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Brax
Massively parallel rigidbody physics simulation
Brax is a fast and fully differentiable physics engine for large-scale rigid body simulations, built on JAX. It is designed for research in reinforcement learning and robotics, enabling efficient simulations and gradient-based optimization. -
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Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
Flux is an elegant approach to machine learning. It's a 100% pure Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing and fusing kernels for the GPU, for the best performance. -
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Mathematics Dataset
This dataset code generates mathematical question and answer pairs
...The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. The data can be accessed via PyPI or generated locally using provided Python scripts, with outputs formatted for direct use in training or evaluation pipelines. -
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FLEXible
Federated Learning (FL) experiment simulation in Python
FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees. -
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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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Python Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, source code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search,... -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses:... -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any... -
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Koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code
Koila is a lightweight Python library designed to help developers avoid memory errors when training deep learning models with PyTorch. The library introduces a lazy evaluation mechanism that delays computation until it is actually required, allowing the framework to better estimate the memory requirements of a model before execution. By building a computational graph first and executing operations only when necessary, koila reduces the risk of running out of GPU memory during the forward pass of neural network training. ... -
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AutoMLOps
Build MLOps Pipelines in Minutes
AutoMLOps is a service that generates, provisions, and deploys CI/CD integrated MLOps pipelines, bridging the gap between Data Science and DevOps. AutoMLOps provides a repeatable process that dramatically reduces the time required to build MLOps pipelines. The service generates a containerized MLOps codebase, provides infrastructure-as-code to provision and maintain the underlying MLOps infra, and provides deployment functionalities to trigger and run MLOps pipelines. AutoMLOps gives... -
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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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TensorRT Backend For ONNX
ONNX-TensorRT: TensorRT backend for ONNX
Parses ONNX models for execution with TensorRT. Development on the main branch is for the latest version of TensorRT 8.4.1.5 with full dimensions and dynamic shape support. For previous versions of TensorRT, refer to their respective branches. Building INetwork objects in full dimensions mode with dynamic shape support requires calling the C++ and Python API. Current supported ONNX operators are found in the operator support matrix. For building within docker, we recommend using and setting... -
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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.
