Search Results for "machine learning platform" - Page 19
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Showing 668 open source projects for "machine learning platform"
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jMIPS
MIPS processor in Java
A MIPS processor modeled in Java for educational purposes and aimed at undergraduate Computer Architecture courses. It presently runs MIPS 32-bit R3000 ELF executables on any platform where Java itself will run. Five successively more sophisticated models of a pipelined MIPS RISC processor are included. Though the code is deliberately kept clear and simple in order that readers are able to figure out how a real processor works through the medium of the code, it will soon be able to run a... -
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React 360
Create amazing 360 and VR content using React
React 360 is a framework for the creation of interactive 360 experiences that run in your web browser. It pairs modern APIs like WebGL and WebVR with the declarative power of React, producing applications that can be consumed through a variety of devices. Leveraging web technologies and the existing React ecosystem, React 360 aims to simplify the construction of cross-platform 360 experiences. We've designed the React 360 developer experience to get your first project up and running in only... -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. ... -
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electron-vue-admin
Vue electron admin template web
...It is based on the well-known vue-admin-template and generated with electron-vue tooling via Vue CLI. The project aims to let teams reuse familiar Vue components, routing, and permission models while packaging the app as a cross-platform desktop application. Because it inherits from the template ecosystem, you can incorporate patterns like Element UI components, axios-based API calls, and role-based navigation. It’s useful for internal tools that benefit from native-like desktop packaging, offline capabilities, or OS-level integrations. The repository provides a recognizable structure so Vue developers can get productive quickly without learning a new desktop-specific framework from scratch. -
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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Edward
A probabilistic programming language in TensorFlow
...It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. Expectation-Maximization, pseudo-marginal and ABC methods, and message passing algorithms. -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested... -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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Caffe Framework
Caffe, a fast open framework for deep learning
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
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TextTeaser
TextTeaser is an automatic summarization algorithm
...By combining these features with a simple scoring mechanism, it produces summaries that are both readable and informative. Originally inspired by research and earlier implementations, textteaser provides a lightweight solution for summarization without requiring heavy machine learning models. It is particularly useful for developers, researchers, or content platforms seeking a simple, rule-based approach to article summarization. -
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Tangent
Source-to-source debuggable derivatives in pure Python
...In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy. -
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The Edge Machine Learning library
Machine learning algorithms for edge devices
Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency, and energy. One instance of where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.These algorithms can train models for classical supervised learning problems with memory requirements that are orders of magnitude lower than other modern ML algorithms. ... -
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LocoKit
Location, motion, and activity recording framework for iOS
Machine Learning-based activity type detection. Improved detection of Core Motion activity types (stationary, walking, running, cycling, automotive). Distinguish between specific transport types (car, train, bus, motorcycle, airplane, boat). Optionally produce high level Path and Visit timeline items, to represent the recording session at human level. -
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SynthePG
Live Music Compositor learning base on TPG
This android app is producing music and partition sheets in real time. Furthermore, the AI module can be activated and be trained to learn you prefered style and play it. You can also team play your music with the interconnect mode. Finally, move your phone like a 'chef d'orchestre' and see how it changes the music... -
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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
...The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile. After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. -
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mui
High-performance framework closest to native APP experience
The gap between performance and experience has always been the primary reason for mobile app developers to abandon HTML5. The browser’s natural page-cutting white screen, the unbearable turning animation, the jittering of floating elements, and the inability to pull down to refresh smoothly, all of which frustrate HTML5 developers, especially when they get a low-end Android machine to run, and the phone falls. On the other hand, the browser default control styles are few and ugly, making a... -
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A Complete Beginner's Guide to Django
A Complete Beginner's Guide to Django - Code Samples
Code samples from the Django tutorial series. I’m starting a new tutorial series about Django fundamentals. It’s a complete beginner’s guide to start learning Django. The material is divided into seven parts. We’re going to explore all the basic concepts in great detail, from installation, and preparation of the development environment, models, views, templates, URLs to more advanced topics such as migrations, testing, and deployment. I wanted to do something different. A tutorial that would... -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries. ... -
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Nanobox
The ideal platform for developers
Nanobox automates the creation of isolated, repeatable environments for local and production applications. When developing locally, Nanobox provisions your app's infrastructure inside of a virtual machine (VM) and mounts your local codebase into the VM. Any changes made to your codebase are reflected inside the virtual environment. Once code is built and tested locally, Nanobox provisions and deploys an identical infrastructure on a production platform. Nanobox uses Virtual Box and Docker to create virtual development environments on your local machine. ... -
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VBScript Development Kit (VDK)
A VBScript development library for serious VBScript programmers
The VBScript Development Kit (VDK) is a VBScript compliant development library to be used in any host that has support for Windows Script Host (WSH). Modelled after the Java Development Kit (JDK), the VDK adds programming features/concepts prevalent in other higher order programming languages such as Java, C#, etc. The VDK provides rapid development capabilities by providing out of the box support for many common programming activities e.g., string manipulation and handling, file... -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
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Eve
Better tools for thought
Eve is a programming language and IDE based on years of research into building a human-first programming platform. From code embedded in documents to a language without order, it presents an alternative take on what programming could be - one that focuses on us instead of the machine. We've made tremendous advancements since the early days of programming, but the modern form still forces us into the role of bit shifting machines. Programming has always been for us - the humans tapping at keyboards. ... -
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Skadoosh
A project that highly compresses android sources and uploads them here
Most Android platform developers have many problems, like slow internet or even low bandwidth. Due to this, they are unable to sync the big chunk of Android source codes from their respective repositories. Hence, most of the new and learning developers just fail at the first step which is downloading the source code! So, I started this project where people needing the sources just submit a pull request of they manifest required. -
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Caffe
A fast open framework for deep learning
Caffe is an open source deep learning framework that’s focused on expression, speed and modularity. It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and...