Open Source Linux Machine Learning Software - Page 30
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Machine Learning Software for Linux
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LightFM
A Python implementation of LightFM, a hybrid recommendation algorithm
LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high-quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalize to new items (via item features) and to new users (via user features). -
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LightPCC
Parallel pairwise correlation computation on Intel Xeon Phi clusters
The first parallel and distributed library for pairwise correlation/dependence computation on Intel Xeon Phi clusters. This library is written in C++ template classes and achieves high speed by exploring the SIMD-instruction-level and thread-level parallelism within Xeon Phis as well as accelerator-level parallelism among multiple Xeon Phis. To facilitate balanced workload distribution, we have proposed a general framework for symmetric all-pairs computation by building provable bijective functions between job identifier and coordinate space for the first time. -
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LightSpMV
lightweight GPU-based sparse matrix-vector multiplication (SpMV)
LightSpMV is a novel CUDA-compatible sparse matrix-vector multiplication (SpMv) algorithm using the standard compressed sparse row (CSR) storage format. We have evaluated LightSpMV using various sparse matrices and further compared it to the CSR-based SpMV subprograms in the state-of-the-art CUSP and cuSPARSE. Performance evaluation reveals that on a single Tesla K40c GPU, LightSpMV is superior to both CUSP and cuSPARSE, with a speedup of up to 2.60 and 2.63 over CUSP, and up to 1.93 and 1.79 over cuSPARSE for single and double precision, respectively. -
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Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time. -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. PyTorch Lightning, a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code. Hydra, a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. You should expect it to be 33% faster and save up to 40% memory. Aim is an open-source experiment tracker that logs your training runs, and enables a beautiful UI to compare them. -
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Lihang
Statistical learning methods (2nd edition) [Li Hang]
Lihang is an open-source repository that provides educational notes, mathematical derivations, and code implementations based on the book Statistical Learning Methods by Li Hang. The repository aims to help readers understand the theoretical foundations of machine learning algorithms through practical implementations and detailed explanations. It includes notebooks and scripts that demonstrate how key algorithms such as perceptrons, decision trees, logistic regression, support vector machines, and hidden Markov models work in practice. In addition to code examples, the project contains supplementary materials such as formula references, glossaries of technical terms, and documentation explaining mathematical notation used throughout the algorithms. The repository also provides links to related research papers and references that expand on the theoretical background presented in the book. -
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Linear Regression
linear regression along with plotting
Linear regression with plotting facility for simple and 2d regression. linear_regression.c is the main file and Include the library lib_sim_eq.c. You must have installed gnuplot prior to compiling the program. -
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Linfa
A Rust machine learning framework
linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust. Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks. -
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Lita
A robot companion for your company's chat room
Lita is a chat bot written in Ruby that brings more fun and efficiency to your favorite chat service. Through its plugin system, Lita can be connected to different chat services and display new behavior preferred by those who use it. It's ideal for businesses that want a chat service that is not only efficient, but friendly and personalized as well. Lita can become your very own robot companion, tailor-made for your business. Lita can be customized according to your company's culture and needs. It can be used to automate various time-consuming and error-prone tasks, while also letting your company members have fun and create a sense of community. -
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Literature of Deep Learning for Graphs
A comprehensive collection of recent papers on graph deep learning
Literature of Deep Learning for Graphs is a curated repository that collects research papers and educational resources related to deep learning methods for graph-structured data. The project organizes important academic work covering topics such as graph neural networks, graph embeddings, knowledge graphs, and network representation learning. By structuring the literature into categories, the repository allows researchers to quickly identify influential papers in specific subfields of graph machine learning. The collection includes foundational works that introduced graph convolutional networks as well as more recent research on large-scale graph representation learning and graph generation techniques. The repository is designed as a reference guide for students and researchers who want to explore the rapidly growing field of graph deep learning. -
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Llama Cookbook
Solve end to end problems using Llama model family
The Llama Cookbook is the official Meta LLaMA guide for inference, fine‑tuning, RAG, and multi-step use-cases. It offers recipes, code samples, and integration examples across provider platforms (WhatsApp, SQL, long context workflows), enabling developers to quickly harness LLaMA models -
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Llama Recipes
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT method
The 'llama-recipes' repository is a companion to the Meta Llama models. We support the latest version, Llama 3.1, in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run Llama locally, in the cloud, and on-prem. -
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LlamaChat
Chat with your favourite LLaMA models in a native macOS app
Chat with your favourite LLaMA models, right on your Mac. LlamaChat is a macOS app that allows you to chat with LLaMA, Alpaca, and GPT4All models all running locally on your Mac. -
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Lucid
A collection of infrastructure and tools for research
Lucid is a collection of infrastructure and tools for research in neural network interpretability. Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support. Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Collaboratory. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. You can run the notebooks on your local machine, too. Clone the repository and find them in the notebooks subfolder. You will need to run a local instance of the Jupyter notebook environment to execute them. Feature visualization answers questions about what a network, or parts of a network, are looking for by generating examples. -
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Ludwig
A codeless platform to train and test deep learning models
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data. -
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Lumi-HSP
This is an AI language model that can predict Heart failure or stroke
Using thsi AI model, you can predict the chances of heart stroke and heart failure. HIGLIGHTS : 1. Accuracy of this model is 95% 2. This model uses the powerful Machine Learning algorithm "GradientBoosting" for predicting the outcomes. 3. An easy to use model and accessible to everyone. -
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Lumixon
This AI can answer any information based questions from the user.
1. This AI is not yet prepared for human interactions or chatting. 2. The AI produces the complete information regarding the user's question and if you wish to search for another new question, you need to close and run the application again. This feature will be changed in the next release 3. Download and extract the files to your desired location and run the exe file in order to run the application. 4. The AI prints the website(s) links if it is unable to get an answer for the user's question. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes obligatory when running a model. Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. Graph level memory allocation optimization and buffer reuse are supported. The core library tries to keep minimum external dependencies to keep the library footprint small. -
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MACSY
Modular Architecture for Cognitive Systems
Macsy is a framework for developing modular agents. Data is organised in blackboards. Computations are performed by modules that annotate the data in the blackboards. Modules communicate indirectly through the annotations that they leave in the blackboards. The framework enables the development of decentralised software agents for a plethora of applications. -
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MCTIMME
Microbial Counts Trajectories Infinite Mixture Model Engine
MCTIMME is a nonparametric Bayesian computational framework for analyzing microbial time-series data.The current implementation is in Matlab. -
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MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
MEDIUM_NoteBook is an open-source repository that contains a collection of Jupyter notebooks and code examples originally developed to accompany technical articles published on Medium. The project provides practical demonstrations of machine learning algorithms, data analysis workflows, and visualization techniques. Each notebook typically focuses on explaining a specific concept through step-by-step examples that combine explanatory text, code, and visual outputs. The repository covers a wide variety of data science topics such as predictive modeling, data preprocessing, statistical analysis, and feature engineering. Because the notebooks are designed as educational materials, they often emphasize readability and reproducibility so that readers can easily run and modify the examples. The project is useful for learners who want to explore machine learning concepts interactively using Python and common data science libraries. -
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A High-Order Multi-Variate Approximation Scheme for Arbitrary Data Sets, C implementation of the method described in http://web.mit.edu/qiqi/www/paper/interpolation.pdf, with Python and Fortran interfaces.
