Search Results for "machine learning platform" - Page 15
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Showing 571 open source projects for "machine learning platform"
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results. ... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. -
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SageMaker Containers
Create SageMaker-compatible Docker containers
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. -
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Keystone Engine
Keystone assembler framework: Core (Arm, Arm64, Hexagon, Mips, etc.)
Keystone is a lightweight multi-platform, multi-architecture assembler framework. Multi-architecture, with support for Arm, Arm64 (AArch64/Armv8), Ethereum Virtual Machine, Hexagon, Mips, PowerPC, Sparc, SystemZ, & X86 (include 16/32/64bit). Clean/simple/lightweight/intuitive architecture-neutral API. Implemented in C/C++ languages, with bindings for Java, Masm, Visual Basic, C#, PowerShell, Perl, Python, NodeJS, Ruby, Go, Rust, Haskell & OCaml available. -
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ocapi
C-level driver collection for MSP430 and derivatives
...Current modules support serial communication via UART, I2C and SPI, digital I/O (GPIO), analog-to-digital conversion (ADC), Timers and so on. ocapi aims at being independent of the development host platform. So it should work, no matter if you live in the Windows, Linux or Mac world. ocapi is used as the hardware abstraction layer for the MSP430 port of the Takatuka Java virtual machine (JVM). Before it can be used, the library must be built from the sources provided here. The build environment allows for fine grained configuration of the library's functionality and target system. -
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the... -
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Multrin
Organize apps windows in tabs like in abandoned Windows Sets and more
Multrin is a cross-platform app built on Electron that lets you to organize apps in tabs by simply dropping them onto Multrin. It aims to greatly improve your productivity and organization. Multrin works currently only on Windows and macOS. Support for Linux coming soon. Before running Multrin in development mode, please ensure you have Node.js installed on your machine. -
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The Neural Process Family
This repository contains notebook implementations
Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. ... -
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CrypTen
A framework for Privacy Preserving Machine Learning
CrypTen is a research framework developed by Facebook Research for privacy-preserving machine learning built directly on top of PyTorch. It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. ... -
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ChainerRL
ChainerRL is a deep reinforcement learning library
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments... -
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PRMLT
Matlab code of machine learning algorithms in book PRML
This Matlab package implements machine learning algorithms described in the great textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML). It is written purely in Matlab language. It is self-contained. There is no external dependency. This package requires Matlab R2016b or latter, since it utilizes a new Matlab syntax called Implicit expansion (a.k.a. broadcasting). -
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rpackage conjurer
Synthetic data generation using R
Builds synthetic data applicable across multiple domains. This package also provides flexibility to control data distribution to make it relevant to many industry examples. -
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GHC (Glasgow Haskell Compiler)
Mirror of the Glasgow Haskell Compiler
GHC (Glasgow Haskell Compiler) is the leading open-source compiler and interactive environment for the Haskell programming language, supporting the Haskell 2010 standard plus numerous language extensions. It compiles to native machine code (via LLVM or C), and includes the interactive GHCi REPL. For full information on building GHC, see the GHC Building Guide. Here follows a summary - if you get into trouble, the Building Guide has all the answers. For building library documentation, you'll... -
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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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X11workbench
X11 developer's 'workbench' and lightweight toolkit API
(preliminary) X11 developer's 'workbench' application using a lightweight statically linked custom toolkit that is intended to be used by X11 applications built with the X11 Workbench. The primary goal of the toolkit is ease of use (short learning curve), lightweight self-contained executables, UI speed, cross platform compatibility, and minimal dependencies. The primary goal of the workbench is to provide an editor on X11 platforms that integrates development, provides rapid development tools (like 'wizards' and safe X11 debugging), and allows you to move the cursor past the end of a line without creating 'end of line' whitespace or 'bouncing' the cursor horizontally while scrolling vertically. ... -
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12306 Ticket Buying Assistant
12306 Smart ticket swiping, ticket booking
...It includes support for account management, login handling, and CAPTCHA-solving integrations to mimic real user behavior. The project is often used as both a practical utility and a learning resource for understanding HTTP automation and web interaction. -
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NativeScript Documentation
Documentation, API reference, and code snippets for NativeScript
...AndroidTV and Watch development watchOS development. Learning native platforms through JavaScript understanding. Exploring platform API documentation by trying APIs directly from a web browser without requiring a platform development machine setup. -
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TensorFlow Haskell
Haskell bindings for TensorFlow
The tensorflow-haskell package provides Haskell-language bindings for TensorFlow, giving Haskell developers the ability to build and run computation graphs, machine learning models, and leverage TensorFlow's ecosystem—though it is not an official Google release. As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests. Run the install_macos_dependencies.sh script in the tools/ directory. The script installs dependencies via Homebrew and then downloads and installs the TensorFlow library on your machine under /usr/local. ... -
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X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
X-DeepLearning (XDL for short) is a complete set of deep optimization solutions for high-dimensional sparse data scenarios (such as advertising/recommendation/search, etc.). XDL version 1.2 has been released recently. Performance optimization for large batch/low concurrency scenarios, 50-100% performance improvement in such scenarios. Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely... -
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. ... -
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments... -
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Facets
Visualizations for machine learning datasets
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. -
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pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. ...