Search Results for "classification" - Page 13
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Showing 641 open source projects for "classification"
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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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NLP Best Practices
Natural Language Processing Best Practices & Examples
In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive business adoption of artificial intelligence (AI) solutions. In the last few years, researchers have been applying newer deep learning methods to NLP. Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora. This repository contains examples and... -
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An alignment-free standalone tool with interactive graphical user interface for DNA sequence comparison and analysis
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VoteNet
Deep Hough Voting for 3D Object Detection in Point Clouds
VoteNet is a 3D object detection framework for point clouds that combines deep point set networks with a Hough voting mechanism to localize and classify objects in 3D space. It tackles the challenge that object centroids in 3D scenes often don’t lie on any input surface point by having each point “vote” for potential object centers; these votes are then clustered to propose object hypotheses. Once cluster centers are formed, the network regresses bounding boxes around them and classifies... -
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ResNeXt
Implementation of a classification framework
ResNeXt is a deep neural network architecture for image classification built on the idea of aggregated residual transformations. Instead of simply increasing depth or width, ResNeXt introduces a new dimension called cardinality, which refers to the number of parallel transformation paths (i.e. the number of “branches”) that are aggregated together. Each branch is a small transformation (e.g. bottleneck block) and their outputs are summed—this enables richer representation without excessive parameter blowup. ... -
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PyHubs
Hubness-aware machine learning in Python
PyHubs is a machine learning library developed in Python containing implementations of hubness-aware machine learning algorithms together with some useful tools for machine learning experiments. According to our recent observation, old versions of PyHubs (such as 1.2.1) does not provide correct results with new versions of numpy (such as 1.16), however, we think that the most recent version of PyHubs (1.3) works correctly with new versions of numpy as well. -
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StarSpace
Learning embeddings for classification, retrieval and ranking
...The library supports a variety of tasks (text classification, nearest-neighbor search, recommendation, entity linking) with simple configuration. It includes efficient batching, negative sampling strategies, and on-the-fly embedding updates. -
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EverydayWechat
Python tool that automates WeChat messages, replies, & group utilities
...In addition to personal messaging automation, the project includes a group assistant that can respond to queries and provide useful information within chat groups. These group utilities can retrieve data such as weather conditions, calendar details, garbage classification information, movie box office statistics, delivery tracking updates, and air quality reports. -
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TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
...Each section focuses on a different aspect of machine learning development, including tensor manipulation, model training, optimization strategies, and data processing techniques. The examples illustrate how TensorFlow operations and tensors can be used to build machine learning pipelines and perform tasks such as regression, classification, and clustering. By combining theoretical explanations with executable code, the project helps developers understand how TensorFlow algorithms operate internally while also providing working examples that can be adapted for real projects. -
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Machine Learning with TensorFlow
Accompanying source code for Machine Learning with TensorFlow
...The project provides numerous code samples demonstrating how to build machine learning models using the TensorFlow framework. These examples illustrate core machine learning concepts such as regression, classification, clustering, and neural networks through practical implementations. The repository includes implementations of algorithms such as logistic regression, convolutional neural networks, and autoencoders, which allow readers to experiment with different learning techniques. Many examples are structured as standalone scripts or notebooks that can be executed directly to reproduce the results described in the book. ... -
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MothurR
This program mainly produces R scripts using Mothur result files.
The program generates R scripts to draws line/bar graphs using Mothur diversity and classification files, to calculate Shannon indexes, to perform PCA, Mann-Whitney U test, and pMANOVA test. -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
...It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. ... -
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MyCoRe
your repository framework
MyCoRe is an Open Source project for the development of Repositories, Digital Library and archive solutions. The technical base of the system is formed of Java class libraries, XML technology and different database backends. Since 2015 we use https://mycore.atlassian.net/ for bug tracking. Please use our ticket system there. -
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Texar
Toolkit for Machine Learning, Natural Language Processing
...Texar-TensorFlow (this repo) and Texar-PyTorch have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Rich Pre-trained Models, Rich Usage with Uniform Interfaces. BERT, GPT2, XLNet, etc, for encoding, classification, generation, and composing complex models with other Texar components! -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
...It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. The code demonstrates how to process frame-level features, train logistic and deep learning models, evaluate them using metrics like global Average Precision (gAP) and mean Average Precision (mAP), and export trained models for MediaPipe inference. -
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Chatito
Dataset generation for AI chatbots, NLP tasks
Chatito is a tool that helps generate datasets for training and validating chatbot models using a simple domain-specific language (DSL). -
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GSMLBook
Recipes for basic machine learning algorithms using sklearn in jupyter
...The emphasis is primarily on learning to use existing libraries such as Scikit-Learn with easy recipes and existing data files that can found on-line. Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests; neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. Appendices provide a review of probability and linear algebra. ... -
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ELI5
A library for debugging/inspecting machine learning classifiers
...The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance for black-box models. It also provides specialized tools such as TextExplainer, which can highlight important words in text classification tasks to explain why a model produced a particular prediction. Additionally, the library integrates explanation algorithms such as LIME to interpret predictions from arbitrary machine learning models. -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
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MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning
...The project also notes that MAML can be difficult to train and presents the implementation as a practical starting point for research. Overall, it is useful for students and researchers who want to study fast adaptation, few-shot classification, and gradient-based meta-learning in PyTorch. -
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Medrechaincode
Lifetime medical records in decentralized chain code-network
...This platform will also help the healthcare professional to use integrated data quality tool to remove duplicity of patient records , profiling of medical records, metadata discovery , data cleansing , classification of medical records, bucketization, anomaly discovery to find out the anomalous trend of medical records. m This platform will help patient to reduced their consultation time, monetize their medical records, for labs to get the historical records & come up with new medicine and make a clinical trial. -
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MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
...The training and evaluation pipeline is lightweight and fast, so experimenting with different languages or initialization strategies is easy. Beyond dictionary induction, the learned embeddings are often used as building blocks for downstream tasks like classification, retrieval, or machine translation. -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
...Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s machine learning package. The repository contains detailed analyses of various algorithms including classification, regression, clustering, dimensionality reduction, and recommendation systems. Each section discusses both the mathematical principles behind the algorithms and how Spark implements them in a distributed computing environment. By studying these implementations, readers gain insight into how large-scale machine learning pipelines operate across distributed data systems. -
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Deep Learning for Medical Applications
Deep Learning Papers on Medical Image Analysis
Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. ...
