Search Results for "machine learning python" - Page 83
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Showing 3051 open source projects for "machine learning python"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Phenalysis
Analyze agronomic plant research plots in aerial orthomosaic images.
A graphical user interface to import, analyze and export plots from orthomosaic images of agronomic trials. Please cite the following reference in your work if you use Phenalysis: Khan Z and Miklavcic SJ (2019) An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images. Front. Plant Sci. 10:683. doi: https://doi.org/10.3389/fpls.2019.00683 This tool is being developed through the sponsorship of the Australian Research Council's Industrial Transformation Research Hub on... -
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Image Quality Assessment
Convolutional Neural Networks to predict aesthetic quality of images
Image Quality Assessment is an open-source deep learning project that implements neural models for predicting the aesthetic and technical quality of digital images. The repository provides an implementation inspired by the NIMA (Neural Image Assessment) research approach, which uses convolutional neural networks trained on human-annotated datasets to estimate image quality scores. The goal of the project is to automatically evaluate images based on perceived quality factors such as... -
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AI Cheatsheets
Essential Cheat Sheets for deep learning and machine learning research
cheatsheets-ai is an open-source repository that collects essential cheat sheets covering many tools and concepts used in machine learning, deep learning, and data science. The project aims to provide quick-reference materials that help engineers, researchers, and students review key techniques and frameworks without reading extensive documentation. It compiles cheat sheets for widely used libraries and technologies such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, and PySpark. ... -
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Speech Recognition in English & Polish
Speech recognition software for English & Polish languages
Software for speech recognition in English & Polish languages. Basic versions of SkryBot: 1. SkryBot Home Speech (English Language) - https://sourceforge.net/projects/skrybotdomowy/files/ReleasesEnglish/InstalatorSkryBotHomeSpeechDemo-2.6.9.18117.exe/download 2. SkryBot DoMowy (Polish Language) - https://sourceforge.net/projects/skrybotdomowy/files/ReleasesPolish/InstalatorSkryBotDoMowyDemo-2.4.9.18117.exe/download More... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Web Security Dojo
Virtual training environment to learn web app ethical hacking.
Web Security Dojo is a virtual machine that provides the tools, targets, and documentation to learn and practice web application security testing. A preconfigured, stand-alone training environment ideal for classroom and conferences. No Internet required to use. Ideal for those interested in getting hands-on practice for ethical hacking, penetration testing, bug bounties, and capture the flag (CTF). A single OVA file will import into VirtualBox and VMware. There is also an Ansible... -
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SMAC
SMAC: The StarCraft Multi-Agent Challenge
SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL. -
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TensorFlow Course
Simple and ready-to-use tutorials for TensorFlow
This repository houses a highly popular (~16k stars) set of TensorFlow tutorials and example code aimed at beginners and intermediate users. It includes Jupyter notebooks and scripts that cover neural network fundamentals, model training, deployment, and more, with support for Google Colab. -
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Docker Machine
Machine management for a container-centric world
Docker Machine is a tool that lets you install Docker Engine on virtual hosts, and manage the hosts with docker-machine commands. You can use Machine to create Docker hosts on your local Mac or Windows box, on your company network, in your data center, or on cloud providers like Azure, AWS, or DigitalOcean. Using docker-machine commands, you can start, inspect, stop, and restart a managed host, upgrade the Docker client and daemon, and configure a Docker client to talk to your host. Point... -
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
PyTorch-NLP is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out... -
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nGraph
nGraph has moved to OpenVINO
Frameworks using nGraph Compiler stack to execute workloads have shown up to 45X performance boost when compared to native framework implementations. We've also seen performance boosts running workloads that are not included on the list of Validated workloads, thanks to nGraph's powerful subgraph pattern matching. Additionally, we have integrated nGraph with PlaidML to provide deep learning performance acceleration on Intel, nVidia, & AMD GPUs. nGraph Compiler aims to accelerate developing... -
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ELI5
A library for debugging/inspecting machine learning classifiers
ELI5 is a Python library designed to help developers interpret, debug, and explain the predictions of machine learning models. The project focuses on improving model transparency by providing tools that visualize feature importance and prediction reasoning. It supports several popular machine learning frameworks including scikit-learn, XGBoost, LightGBM, CatBoost, and Keras. -
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MatchZoo
Facilitating the design, comparison and sharing of deep text models
The goal of MatchZoo is to provide a high-quality codebase for deep text matching research, such as document retrieval, question answering, conversational response ranking, and paraphrase identification. With the unified data processing pipeline, simplified model configuration and automatic hyper-parameters tunning features equipped, MatchZoo is flexible and easy to use. Preprocess your input data in three lines of code, keep track parameters to be passed into the model. Make use of MatchZoo... -
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BlobCity
A blazing fast ACID compliant NoSQL DataLake
BlobCity DB is an AI-optimized, NoSQL database designed for high-performance analytics and machine learning workloads. It combines structured and unstructured data storage, offering fast query execution and seamless integration with AI frameworks. It is built to handle large-scale datasets efficiently. -
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distribution-is-all-you-need
The basic distribution probability Tutorial for Deep Learning Research
...The material highlights relationships such as conjugate priors and special-case distributions. Examples connect probability functions with machine learning concepts including binary and multiclass cross-entropy. An overview image and presentation summarize the distribution families and their connections for quick reference. -
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This project relates to research work at Imperial College conducted by members of the SPIKE (Structured and Probabilistic Intelligent Knowledge Engineering), including in particular logic-based learning systems such as TAL, ASPAL and ILASP.
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YouTube-8M
Starter code for working with the YouTube-8M dataset
youtube-8m is Google’s open source starter code and reference implementation for training and evaluating machine learning models on the YouTube-8M dataset, one of the largest video understanding datasets publicly released. The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. ... -
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NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging... -
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GSMLBook
Recipes for basic machine learning algorithms using sklearn in jupyter
This is an introductory book in machine learning with a hands on approach. It uses Python 3 and Jupyter notebooks for all applications. The emphasis is primarily on learning to use existing libraries such as Scikit-Learn with easy recipes and existing data files that can found on-line. Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests; neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. ... -
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DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
DeepSDF is a deep learning framework for continuous 3D shape representation using Signed Distance Functions (SDFs), as presented in the CVPR 2019 paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation by Park et al. The framework learns a continuous implicit function that maps 3D coordinates to their corresponding signed distances from object surfaces, allowing compact, high-fidelity shape modeling. Unlike traditional discrete voxel grids or meshes, DeepSDF... -
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nlp-roadmap
ROADMAP(Mind Map) and KEYWORD for students
nlp-roadmap is a visual learning guide for students exploring natural language processing and related foundations. It organizes essential keywords into semantic mind maps rather than presenting a conventional textbook or code library. The material begins with probability and statistics, then progresses through machine learning and text mining. Its final section maps major concepts and methods within natural language processing. -
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RoboSchool
Open source software for robot simulation, integrated with OpenAI Gym
Roboschool is a set of open source robot simulation environments for reinforcement learning, created as an alternative to the Mujoco physics engine. It integrates with OpenAI Gym and provides a variety of continuous control tasks, including humanoid locomotion, quadrupeds, and robotic arms. The library is built on the Bullet Physics engine, making it accessible without the licensing requirements of Mujoco. Roboschool includes training scripts and examples for applying reinforcement learning... -
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AIAlpha
Use unsupervised and supervised learning to predict stocks
...The project typically involves collecting market data, transforming financial indicators into machine learning features, and training models to identify patterns that may predict market trends. It also demonstrates how models can be evaluated through backtesting frameworks that simulate how a strategy would perform using historical market conditions. By combining financial analytics with machine learning algorithms, the repository illustrates the process of building data-driven investment strategies. -
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Activity Recognition
Resources about activity recognition
This repository is a curated collection of resources, papers, code, and summaries relating to human activity recognition/behavior recognition. It is not a single integrated software package but rather a knowledge base organizing feature extraction methods, deep learning approaches, transfer learning strategies, datasets, and representative research in behavior recognition. The repository includes links to code in MATLAB, Python, summaries of algorithms, datasets, and relevant research papers. Feature extraction method summaries (e.g. motion, sensor, vision). Deep learning for activity recognition references. -
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DCVGAN
DCVGAN: Depth Conditional Video Generation, ICIP 2019.
This paper proposes a new GAN architecture for video generation with depth videos and color videos. The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical...
