Search Results for "data structures"
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Showing 23 open source projects for "data structures"
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
AutoMLPipeline (AMLP) is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for ica (Independent Component Analysis) and pca (Principal Component Analysis) transformations, respectively, concatenated with the hot-bit encoding (ohe) of categorical features (catf) of a given data for rf (Random Forest) modeling. -
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The Algorithms - C++ #
Collection of various algorithms in mathematics, machine learning
TheAlgorithms/C-Plus-Plus is a large open-source repository that collects implementations of many classic algorithms and data structures written in the C++ programming language. The project is part of the broader “The Algorithms” initiative, which maintains algorithm implementations in several programming languages to support education and knowledge sharing. Within the C++ repository, contributors implement algorithms across a wide range of fields including sorting, graph theory, number theory, machine learning, cryptography, and data structures. -
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VisualDL
Deep Learning Visualization Toolkit
VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including tracking metrics in real-time, visualizing the model structure, displaying the data sample, visualizing the relationship between hyperparameters and model metrics, presenting the changes of distributions of tensors, showing the pr curves, projecting high-dimensional data to a lower dimensional space and more. ... -
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Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the... -
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RecBole
A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library. We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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Smile
Statistical machine intelligence and learning engine
Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. ... -
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The Algorithms - C #
Collection of various algorithms in mathematics, machine learning
TheAlgorithms/C is an open-source repository that provides implementations of classic algorithms and data structures written in the C programming language. The project is part of the larger “The Algorithms” initiative, which aims to create educational resources by implementing algorithms in multiple programming languages. Within the C repository, contributors implement algorithms from many areas of computer science including sorting, searching, graph processing, mathematics, machine learning, and numerical methods. ... -
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T81 558
Applications of Deep 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. ... -
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3D-Machine-Learning
A resource repository for 3D machine learning
3D-Machine-Learning is an open-source repository that compiles resources related to machine learning techniques applied to three-dimensional data. The project acts as a curated research directory that includes papers, datasets, tutorials, and software tools relevant to the emerging field of 3D machine learning. This interdisciplinary domain combines ideas from computer vision, computer graphics, and deep learning to analyze and generate three-dimensional structures. The repository includes references to important research papers covering topics such as point cloud processing, 3D reconstruction, shape analysis, and scene understanding. ... -
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LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser.... -
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MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes. The framework introduces specialized layers such as edge-based convolution, mesh pooling, and mesh unpooling operations that enable hierarchical feature learning on mesh surfaces. ... -
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
...The project provides a large collection of tools that implement techniques from academic research on financial machine learning. It covers the full lifecycle of developing data-driven trading strategies, including data preprocessing, feature engineering, labeling techniques, model training, and performance evaluation. Many of the algorithms implemented in the library are based on concepts introduced in advanced quantitative finance literature and peer-reviewed research. The library also includes tools for constructing specialized financial data structures, generating predictive features, and evaluating trading strategies through backtesting. ... -
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend. The template includes configuration files, scripts, and project structures that help teams build reproducible experiments and production-ready pipelines. ... -
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Delta ML
Deep learning based natural language and speech processing platform
...DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train, develop, and deploy NLP and/or speech models. Use configuration files to easily tune parameters and network structures. What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph. Text classification, named entity recognition, question and answering, text summarization, etc. Uniform I/O interfaces and no changes for new models. -
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Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
...Instead of scalar neuron activations, capsules output vectors that encode both the presence of features and their spatial properties such as orientation or pose. The repository implements the dynamic routing algorithm between capsules, which allows lower-level features to route their outputs to higher-level structures that best represent the detected patterns. This approach enables the model to capture part-to-whole relationships in visual data more effectively than standard CNNs. The project serves primarily as a research implementation that demonstrates how capsule networks can be built and trained using modern deep learning frameworks. -
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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). The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. ... -
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DGRLVQ
Dynamic Generalized Relevance Learning Vector Quantization
Some of the usual problems for Learning vector quantization (LVQ) based methods are that one cannot optimally guess about the number of prototypes required for initialization for multimodal data structures i.e.these algorithms are very sensitive to initialization of prototypes and one has to pre define the optimal number of prototypes before running the algorithm. If a prototype, for some reasons, is ‘outside’ the cluster which it should represent and if there are points of a different categories in between, then the other points act as a barrier and the prototype will not find its optimum position during training. ... -
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... - MECore is a shell-based system that allows the user to create propositional knowledge bases, to perform a variety of belief change operations, and to query a knowledge base with respect to the principle of optimum entropy. - Log4KR is a library providing data structures to represent knowledge bases in various logic formalisms (propositional, relational, conditional, probabilistic, ...) and providing algorithms to perform reasoning operations
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LAML:Linear Algebra and Machine Learning
A stand-alone Java library for linear algebra and machine learning
...The goal is to build efficient and easy-to-use linear algebra and machine learning libraries. The reason why linear algebra and machine learning are built together is that full control of the basic data structures for matrices and vectors is required to have fast implementation for machine learning methods. Additionally, LAML provides a lot of commonly used matrix functions in the same signature to MATLAB, thus can also be used to manually convert MATLAB code to Java code. -
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Chordalysis
Log-linear analysis (data modelling) for high-dimensional data
...However, due to its exponential nature, previous approaches did not allow scale-up to more than a dozen variables. We present here Chordalysis, a log-linear analysis method for big data. Chordalysis exploits recent discoveries in graph theory by representing complex models as compositions of triangular structures, also known as chordal graphs. Chordalysis makes it possible to discover the structure of datasets with thousands of variables on a standard desktop computer. Associated papers at ICDM 2013, ICDM 2014 and SDM 2015 can be found at http://www.francois-petitjean.com/Research/ YourKit is supporting Chordalysis open source project with its full-featured Java Profiler. ... -
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cognity
A neural network library for Java.
Cognity is an object-oriented neural network library for Java. It's goal is to provide easy-to-use, high level architecture for neural network computations along with reasonable performance.
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