Search Results for "machine learning python" - Page 88
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Showing 3051 open source projects for "machine learning python"
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The GAN Zoo
A list of all named GANs
The GAN Zoo is an open-source repository that compiles a comprehensive list of Generative Adversarial Network models published in research literature. The project began as a community effort to track the rapidly growing number of GAN architectures appearing in machine learning papers. Because new GAN models are frequently introduced in research publications, the repository serves as a convenient catalog that organizes them in one location. The list includes references to many GAN variants along with links to their original research papers and sometimes implementation code. Users can browse the dataset or explore a tabular version that allows filtering by year or searching for specific model names. ... -
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TensorImage
Image classification library for easily training and deploying models
(Visit our github repository at https://github.com/TensorImage/tensorimage for more information) TensorImage is and open source package for image classification. It has a wide range of data augmentation operations that can be performed over training data to prevent overfitting and increase testing accuracy. TensorImage is easy to use and manage as all files, trained models and data are organized within a workspace directory, which you can change at any time in the configuration file,... -
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Evolution Strategies Starter
Code for the paper "Evolution Strategies.."
evolution-strategies-starter is an archived OpenAI research project that provides a distributed implementation of the algorithm described in the paper “Evolution Strategies as a Scalable Alternative to Reinforcement Learning” by Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever. The repository demonstrates how to scale Evolution Strategies (ES) for reinforcement learning tasks using a master-worker architecture, where the master node broadcasts parameters to multiple workers, and the... -
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Pragmatic AI
[Book-2019] Pragmatic AI: An Introduction to Cloud-based ML
Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and R. ... -
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InfoGAN
Code for reproducing key results in the paper
The InfoGAN repository contains the original implementation used to reproduce the results in the paper “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”. InfoGAN is a variant of the GAN (Generative Adversarial Network) architecture that aims to learn disentangled and interpretable latent representations by maximizing the mutual information between a subset of the latent codes and the generated outputs. That extra incentive encourages the... -
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3D ResNets for Action Recognition
3D ResNets for Action Recognition (CVPR 2018)
We uploaded the pretrained models described in this paper including ResNet-50 pretrained on the combined dataset with Kinetics-700 and Moments in Time. We significantly updated our scripts. If you want to use older versions to reproduce our CVPR2018 paper, you should use the scripts in the CVPR2018 branch. -
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Market Reporter
Automatic Generation of Brief Summaries of Time-Series Data
...Install Docker and Docker Compose. Edit envs/docker-compose.yaml according to your environment. Then, launch containers by docker-compose. We recommend to use pipenv to make a Python environment for this project. Suppose you have a database named master on your local machine. Prediction submodule generates a single comment of a financial instrument at specified time by loading a trained model. -
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Meta-Learning-Papers
Meta Learning/Learning to Learn/One Shot Learning/Few Shot Learning
Meta-Learning-Papers is a curated bibliography focused specifically on meta-learning, learning-to-learn, one-shot learning, and few-shot learning, intended for researchers and practitioners interested in this rapidly evolving subfield of machine learning. It catalogs foundational “legacy” papers that introduced key concepts, as well as more recent work that extends meta-learning to new domains or architectures. -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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EECluster
Tool for energy-efficient resource management in HPC clusters
EECluster is software tool for managing the energy-efficient allocation of the cluster resources. EECluster uses a Hybrid Genetic Fuzzy System as the decision-making mechanism that elicits part of its rule base dependent on the cluster workload scenario, delivering good compliance with the administrator preferences. In the latest version, we leverage a more sophisticated and exhaustive model that covers a wider range of environmental aspects and balances service quality and power... -
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Generative Models
Collection of generative models, e.g. GAN, VAE in Pytorch
This project is a comprehensive open-source collection of implementations of various generative machine learning models designed to help researchers and developers experiment with deep generative techniques. The repository contains practical implementations of well-known architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Restricted Boltzmann Machines, and Helmholtz Machines, implemented primarily using modern deep learning frameworks like PyTorch and TensorFlow. ... -
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FilterPy
Python Kalman filtering and optimal estimation library
...NumPy and SciPy handle the numerical work, with Matplotlib commonly used for visualization. The library accompanies the book Kalman and Bayesian Filters in Python and is useful for learning, prototyping, tracking, navigation, and estimation experiments. -
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Scikit-plot
An intuitive library to add plotting functionality to scikit-learn
...Besides, if you ever need to present your results to someone (virtually any time anybody hires you to do data science), you show them visualizations, not a bunch of numbers in Excel. That said, there are a number of visualizations that frequently pop up in machine learning. Scikit-plot is a humble attempt to provide aesthetically challenged programmers (such as myself) the opportunity to generate quick and beautiful graphs and plots with as little boilerplate as possible. -
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virgo
32 bit VIRGO Linux Kernel
Linux kernel fork-off with cloud and machine learning features -
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Mexopencv
Collection and a development kit of matlab mex functions for OpenCV
mexopencv is a collection of MEX functions that provide MATLAB bindings for OpenCV, the popular computer vision library. It enables MATLAB users to access nearly the full range of OpenCV’s C++ API directly from MATLAB, combining the ease of MATLAB scripting with the performance of OpenCV. -
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Learn_Machine_Learning_in_3_Months
This is the code for "Learn Machine Learning in 3 Months"
This repository outlines an ambitious self-study curriculum for learning machine learning in roughly three months, emphasizing breadth, momentum, and hands-on practice. It sequences core topics—math foundations, classic ML, deep learning, and applied projects—so learners can pace themselves week by week. The plan mixes reading, lectures, coding assignments, and small build-it-yourself projects to reinforce understanding through repetition and implementation. ... -
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NLP Tasks
Natural Language Processing Tasks and References
NLP Tasks is a curated reference map of natural language processing problems and supporting learning resources. It organizes a broad range of NLP subjects so researchers and students can quickly discover areas for further study. Topics include speech recognition, text classification, question answering, machine translation, summarization, sentiment analysis, information extraction, and many others. Individual sections link to research papers, datasets, software projects, challenges, and background material. ... -
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NimTorch
PyTorch - Python + Nim
NimTorch is a deep learning library for the Nim programming language, providing bindings to PyTorch for efficient tensor computations and neural network functionalities. -
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GMOL
A tool for 3D genome structure visualization
...This software was built upon the pre-existing Jmol package by Prof. Cheng's group. The software is developed in Prof. Jianlin Cheng's Bioinformatics, Data Mining and Machine Learning Laboratory in the Computer Science Department at the University of Missouri - Columbia, USA. The project is supported by the National Science Foundation (grant no. DBI1149224). If you use GMOL in your research, please cite: Nowotny, Jackson, Avery Wells, Oluwatosin Oluwadare, Lingfei Xu, Renzhi Cao, Tuan Trieu, Chenfeng He, and Jianlin Cheng. ... -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as... -
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Easy Machine Learning
Easy Machine Learning is a general-purpose dataflow-based system
...Our platform Easy Machine Learning presents a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real-world tasks. In the system, a learning task is formulated as a directed acyclic graph (DAG) in which each node represents an operation (e.g. a machine learning algorithm), and each edge represents the flow of the data from one node to its descendants. -
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LearningToCompare_FSL
Learning to Compare: Relation Network for Few-Shot Learning
LearningToCompare_FSL is a PyTorch implementation of the “Learning to Compare: Relation Network for Few-Shot Learning” paper, focusing on the few-shot learning experiments described in that work. The core idea implemented here is the relation network, which learns to compare pairs of feature embeddings and output relation scores that indicate whether two images belong to the same class, enabling classification from only a handful of labeled examples. The repository provides training and... -
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TensorFlow Internals
Open source ebook about TensorFlow kernel and implementation
It is open source ebook about TensorFlow kernel and implementation mechanism, including programming model, computation graph, and distributed training for machine learning. -
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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. -
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dadosSemiotica
Collecter and manager of semiotica annalisis data
This program is a web application to collect and organize data of text analysis. It works with sets of texts and the analysis are done on portions of the length of a sentence. One of the preprocessing modules is based on CoGroo (A LibreOffice & OpenOffice.org Portuguese Grammar Checker).
