Search Results for "machine learning regression" - Page 12
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Showing 390 open source projects for "machine learning regression"
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. ... -
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Pipelines
An experimental programming language for data flow
...Unlike other languages for defining data flow, the Pipeline language requires the implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline while providing access to thousands of active libraries for machine learning, data analysis, and processing. -
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NN-SVG
Publication-ready NN-architecture schematics
Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand. NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages. -
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MITIE
MITIE: library and tools for information extraction
...The current release includes tools for performing named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors. MITIE is built on top of dlib, a high-performance machine-learning library[1], MITIE makes use of several state-of-the-art techniques including the use of distributional word embeddings[2] and Structural Support Vector Machines[3]. MITIE offers several pre-trained models providing varying levels of support for both English, Spanish, and German trained using a variety of linguistic resources (e.g., CoNLL 2003, ACE, Wikipedia, Freebase, and Gigaword). ... -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). ... -
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Functional, Data Science Intro To Python
[tutorial]A functional, Data Science focused introduction to Python
...The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible. The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, command line tools, recommendation engines, Sentiment Analysis and Cloud Computing. -
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virgo
32 bit VIRGO Linux Kernel
Linux kernel fork-off with cloud and machine learning features -
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Git Time Machine
Atom package that allows you to travel back in commit history
...Instead of just opening a commit log or diff, git-time-machine gives an interactive, incremental experience where you can slide through versions and see content changes in place. This is especially helpful when diagnosing when bugs were introduced, exploring regression contexts, or reviewing code evolution visually. For projects with many commits, it accelerates historical exploration, and it is commonly integrated into editors so developers can use it in their working environment. -
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FIND3
High-precision indoor positioning framework, version 3
...(previously just WiFi). Passive scanning built-in (previously required a separate server). Support for Bluetooth scanning in scanning utility (previously just WiFi). Meta-learning with 10 different machine learning classifiers (previously just three). Client uses Websockets+React which reduces bandwidth (and coding complexity). Rolling compression of MAC addresses for much smaller on-disk databases. Data storage in SQLite-database (previously it was BoltDB). Released under MIT license (more commercially compatible than AGPL). ... -
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Tiramisu
Polyhedral compiler for expressing fast and portable data algorithms
...It provides a simple C++ API for expressing algorithms (Tiramisu expressions) and how these algorithms should be optimized by the compiler. Tiramisu can be used in areas such as linear and tensor algebra, deep learning, image processing, stencil computations and machine learning. The Tiramisu compiler is based on the polyhedral model thus it can express a large set of loop optimizations and data layout transformations. Currently, it targets (1) multicore X86 CPUs, (2) Nvidia GPUs, (3) Xilinx FPGAs (Vivado HLS) and (4) distributed machines (using MPI). ... -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. ... -
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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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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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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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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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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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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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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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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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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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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... -
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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...
