Open Source Linux Machine Learning Software - Page 41
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Start Machine Learning in 2026
A complete guide to start and improve in machine learning
Start Machine Learning in 2026 repository is an open educational guide designed to help beginners enter the field of machine learning and artificial intelligence with little or no prior technical background. The project organizes a large collection of learning resources, including online courses, books, tutorials, research articles, and video lectures that explain fundamental AI concepts. Its structure functions as a learning roadmap that gradually introduces essential topics such as programming, mathematics, statistics, neural networks, and modern deep learning techniques. The repository emphasizes flexibility by allowing learners to choose their own path through the material depending on their interests, preferred learning style, and level of prior knowledge. Many of the resources referenced are free or widely accessible, making the guide practical for self-learners who want to study independently without formal coursework. -
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StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery of benchmarking and baseline methods, giving users flexibility in selecting forecasting approaches depending on data characteristics (trend, seasonality, intermittent demand, etc.). Its internal implementation leverages numba to compile performance-critical code to optimized machine-level instructions, which makes the models much faster than many traditional Python counterparts. -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can contain people as nodes and friendships between them as links, with data like a person’s age and the date a friendship was established. StellarGraph supports the analysis of many kinds of graphs. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn. -
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Stochastico is an implementation of stochastic discrimination for pattern recognition, predictive modeling and data mining applications.
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StudioGAN
StudioGAN is a Pytorch library providing implementations of networks
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation. StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea. Moreover, StudioGAN provides an unprecedented-scale benchmark for generative models. The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT, RQ-Transformer), and Diffusion models (LSGM++, CLD-SGM, ADM-G-U). StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. Among these configurations, we formulate 30 GANs as representatives. Each modularized option is managed through a configuration system that works through a YAML file. -
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SuRankCo
Supervised Ranking of Contigs in de novo Assemblies
SuRankCo is a machine learning based software to score and rank contigs from de novo assemblies of next generation sequencing data. It trains with alignments of contigs with known reference genomes and predicts scores and ranking for contigs which have no related reference genome yet. For more details about SuRankCo and its functioning, please see "SuRankCo: Supervised Ranking of Contigs in de novo Assemblies" Mathias Kuhring, Piotr Wojtek Dabrowski, Andreas Nitsche and Bernhard Y. Renard (http://www.biomedcentral.com/1471-2105/16/240/abstract) PLEASE NOTE, it is recommended to read the paper and the readme.txt file before using SuRankCo. Update Jun2015: * Minor changes to enable BAM support. Update Feb2014: * Added support for FASTA/SAM assemblies in addition to ACE/FASTQ(QUAL). NOTE: features of FASTA/SAM assemblies do not include BaseCount, BaseSeqmentCount and ContigQualities yet. -
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Supertagger
Software for assigning supertags.
Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree. -
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Surface Defect Detection Dataset Papers
Constantly summarizing open source dataset and critical papers
At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. -
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SURIKATA (Syntactic Universal Reasoning for Inducing Kolmogorov Abstract Theories Automatically) is a system for searching large spaces of artifacts and inducing algorithms for generating similar artifacts.
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SwaNN
PSO for neural networks
SwaNN is a basic framework for neural networks based on particle swarm optimization (using the Python package PySwarms (https://pyswarms.readthedocs.io/en/latest/). The zip file contains the main programs in SwaNN.py and around 30 examples : - classification - regression - time series forecasting I need some help for class building (I am not an expert in Python nor in OOP), if somebody is interested in it... In Google Colab : https://colab.research.google.com/drive/1u6SOydDUThUrhTfaic2NiyDhh1ZGRJsH?usp=sharing What's new: - the jupyter notebook is reorganized and clean -
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Swarm Wars
Safety in numbers.
REPOSITORY MOVED TO GITHUB: https://github.com/happyjack27/SwarmWars video sample: http://youtu.be/s5mLNbdBQGY A game where you evolve & compete AI swarms. The organisms use swarm intelligence & ant colony optimization. The organisms can communicate through 3-color signaling as well as by laying beacons. They can attack and repair other organisms. They can select mates, and they can gather and distribute food and material. This behavior is controlled by a genetically evolved neural net augmented with online back propagation learning. The back propagation learning uses a reward vector and plasticity matrix that is evolved as part of the genome. Long story short, the AI is pretty frickin' sophisticated. Players can take control of organisms, trade resources and organisms in a market, and aid evolution by selective breeding. -
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SweetOnionCCG2PTBConverter
A tool that converts CCGBank to PTB
Conversion between different grammar frameworks is of great importance to comparative performance analysis of the parsers developed on them. This tool can convert CCG derivations to PTB trees by using Max Entropy models as well as visualizing the tree graphs. The main technical innovation presented here is the effective conversion method which achieves a F score over 95%. -
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the usage of Swift AI. Each resides in their own repository and can be built with little or no configuration. Each module now contains its own documentation. We recommend that you read the docs carefully for detailed instructions on using the various components of Swift AI. The example projects are another great resource for seeing real-world usage of these tools. Swift AI currently depends on Apple's Accelerate framework for vector/matrix calculations and digital signal processing. -
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Swift for TensorFlow
Swift for TensorFlow
Swift for TensorFlow repository contains the open-source implementation of Swift for TensorFlow, a project that integrates machine learning capabilities directly into the Swift programming language. The initiative aims to provide a new programming model for developing machine learning systems by combining the power of TensorFlow with language-level features such as automatic differentiation and strong type systems. By embedding machine learning functionality into the Swift compiler and language design, the project enables developers to write high-performance machine learning models while maintaining the readability and safety of modern programming practices. Swift for TensorFlow also introduces tools that allow developers to compute gradients automatically, which is essential for training neural networks through gradient-based optimization. -
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Synapse Machine Learning
Simple and distributed Machine Learning
SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit, and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of data sources. SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. For production-grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster. -
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A Synthetic Data Generator for producing mixed datasets described by relevant, irrelevant, and redundant features.
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T81 558
Applications of Deep Neural Networks
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. -
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TEES
Turku Event Extraction System
Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information. -
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TEXT2DATA
Text Analytics Platform
Bring Text Analytics Platform that uses NLP (Natural Language Processing) and Machine Learning to your work environment. Extract essential information from your text documents and let Artificial Intelligence save your time. Get detailed and agile reports on your unstructured data. -
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TF2DeepFloorplan
TF2 Deep FloorPlan Recognition using a Multi-task Network
TF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab. This repo contains a basic procedure to train and deploy the DNN model suggested by the paper 'Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention'. It rewrites the original codes from zlzeng/DeepFloorplan into newer versions of Tensorflow and Python. -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
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TFX
TFX is an end-to-end platform for deploying production ML pipelines
TensorFlow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines. Both the components themselves and the integrations with orchestration systems can be extended. TFX components interact with an ML Metadata backend that keeps a record of component runs, input and output artifacts, and runtime configuration. This metadata backend enables advanced functionality like experiment tracking or warm starting/resuming ML models from previous runs. -
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TNN
Uniform deep learning inference framework for mobile
TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's mainstream open source frameworks, and expands the support for X86 and NV GPUs. On the mobile phone, TNN has been used by many applications such as mobile QQ, weishi, and Pitu. As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN inference framework. -
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Talking Head Anime from a Single Image
Demo for the "Talking Head Anime from a Single Image"
Talking Head Anime from a Single Image is a machine learning project that demonstrates how neural networks can animate anime characters using only a single input image. The system generates animated facial expressions and movements by applying pose transformations to a static image of an anime character. The underlying model uses deep learning techniques to predict how different facial features and body parts should move based on pose parameters or input signals. This allows the software to create realistic animated frames while preserving the identity and appearance of the original character. The repository includes demo applications that allow users to interact with the system through graphical controls or webcam input to drive character motion. These demonstrations illustrate how generative neural rendering can be used to build real-time avatar systems for virtual characters. -
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Tangent
Source-to-source debuggable derivatives in pure Python
Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). 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.
