Search Results for "scikit-learn" - Page 11
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Showing 381 open source projects for "scikit-learn"
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
...Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. The project also provides scripts to fetch and preprocess monolingual data, learn BPE codes, and train cross-lingual embeddings that bootstrap unsupervised alignment between languages. Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training. -
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automl-gs
Provide an input CSV and a target field to predict, generate a model
...No black box: you can see exactly how the data is processed, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow. -
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NeuroNER
Named-entity recognition using neural networks
Named-entity recognition (NER) aims at identifying entities of interest in the text, such as location, organization and temporal expression. Identified entities can be used in various downstream applications such as patient note de-identification and information extraction systems. They can also be used as features for machine learning systems for other natural language processing tasks. Leverages the state-of-the-art prediction capabilities of neural networks (a.k.a. "deep learning") Is... -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. ... -
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Islam 99 names of Allah
This Python 3 software help you to learn to 99 name of Allah.
This Python 3 software help you to learn to 99 name of Allah. Information there : https://simple.wikipedia.org/wiki/Names_of_God_in_Islam For Linux or other operating systems download it from the sources and install Python 3 with all needed libraries. Sources : https://github.com/hamdyaea/Islam_99_names_of_Allah Developer - Author : Hamdy Abou El Anein -
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Easy-TensorFlow
Simple and comprehensive tutorials in TensorFlow
...There is a necessity to address the motivations for this project. TensorFlow is one of the deep learning frameworks available with the largest community. This repository is dedicated to suggesting a simple path to learn TensorFlow. In addition to the aforementioned points, the large community of TensorFlow enriches the developers with the answer to almost all the questions one may encounter. Furthermore, since most of the developers are using TensorFlow for code development, having hands-on on TensorFlow is a necessity these days. Tensorboard is a powerful visualization suite that is developed to track both the network topology and performance, making debugging even simpler. -
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Scikit-plot
An intuitive library to add plotting functionality to scikit-learn
Single line functions for detailed visualizations. Scikit-plot is the result of an unartistic data scientist's dreadful realization that visualization is one of the most crucial components in the data science process, not just a mere afterthought. Gaining insights is simply a lot easier when you're looking at a colored heatmap of a confusion matrix complete with class labels rather than a single-line dump of numbers enclosed in brackets. -
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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 generator to structure its latent space in a way where certain latent variables control meaningful, distinct factors (e.g. rotation, width, stroke thickness) in the output images. ... -
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BossSensor
Hide screen when boss is approaching
...The software relies on libraries such as OpenCV, TensorFlow, and Python-based machine learning tools to perform face detection and classification. Training the system requires a dataset of labeled images representing the boss and other people so that the model can learn to differentiate between them. -
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PySptools
Hyperspectral algorithms for Python
A lightweight hyperspectral imaging library that provides developers with spectral algorithms for the Python programming language. New for v0.14.x: a scikit-learn bridge (alpha and partial). The functions and classes are organized by topics: * abundance maps: FCLS, NNLS, UCLS * classification: AbundanceClassification, NormXCorr, KMeans SAM, SID, SVC * detection: ACE, CEM, GLRT, MatchedFilter, OSP * distance: chebychev, NormXCorr, SAM, SID * endmembers extraction: ATGP, FIPPI, NFINDR, PPI * material count: HfcVd, HySime * noise: Savitzky Golay, MNF, whiten * sigproc: bilateral * sklearn: HyperEstimatorCrossVal, HyperSVC and others * spectro: convex hull quotient, features extraction (tetracorder style), USGS06 lib interface * util: load_ENVI_file, load_ENVI_spec_lib, corr, cov and others The library do an extensive use of the numpy numeric library and can achieve good speed. ... -
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Awesome Math
This is the Curriculum for "How to Learn Mathematics Fast"
...The structure is useful both for newcomers who want a starting plan and for practitioners filling specific gaps before tackling ML or deep learning. Overall, it acts as a compact study plan that turns “learn math” from a vague goal into a concrete, achievable path. -
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Learn Python the Hard Way
Concise study notes derived from “Learn Python the Hard Way”
This repository contains concise study notes derived from “Learn Python the Hard Way,” organized to reinforce core Python concepts through small, targeted examples. It emphasizes hands-on practice—short scripts, exercises, and explanations that help cement syntax, data structures, functions, and modules. The notes call out common gotchas, idioms, and style preferences so learners form good habits early. -
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train these models using contrastive loss and triplet loss functions, which encourage embeddings of similar samples to be close while pushing dissimilar samples farther apart. ... -
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Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). -
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Pygorithm
A Python module for learning all major algorithms
A Python module to learn all the major algorithms on the go! Purely for educational purposes. If you are using Python 2.7 use pip instead. Depending on your permissions, you might need to use pip install, user pygorithm to install. To see all the available functions in a module, you can just type help() with the module name as an argument. -
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xpd-ebike
Infineon e-bike controller setup tool
XPD (stands for "eXtended Parameter Designer") is an alternative to "KeyWin e-Bike Lab Parameter Designer" software used to program the so-called "Infineon" e-bike controllers. * It provides some additional features missing in the original program, and is generally easier to use. * It is opensource so you can learn how it works and add unique features to it or customize to suit your own needs. * It is cross-platform: works on Linux, Windows and Android. -
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OpenTeacher is an opensource application that helps you learn a foreign language vocabulary. Just enter some words in your native and foreign language, and OpenTeacher tests you.
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. ... -
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Pyo Synth
A GUI to help with pyo synthesizer scripts manipulation.
Pyo Synth is an open source application that makes the manipulation of pyo scripts easier by letting you control it with a midi keyboard. The interface allows you to setup every control on your keyboard and link them to parameters in your script during runtime. It is also possible to save your progress directly in the pyo script. See manual for more explanation on features. -
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CWOnly
Python program for sending custom CW macros via serial port
This is a project I started to learn Python and Tkinter. It all started when I received a PI for Christmas in 2015. Over Christmas breaks I have time away from work to play on projects and I have taken an interest in programming. There are many fine ham radio programs out there. This is just a learning tool. The code was written in Python2.7 and bundled with Pyinstaller. -
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Ubuntu Lite SDK
App creator for Ubuntu
Given the richness and variety of the Linux platform, programming on Linux should be easy and fun. However, it's not easy and fun because it is too hard to learn. It is too hard to learn because there are too many choices, and too much information to wade through. Ubuntu lite SDK strives to make programming easy and fun by bringing opinionated choices about how to write different kinds of programs to developers. Included is a Ubuntu application template for making applications that integrate smoothly into the Ubuntu software infrastructure. -
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Machine Learning framework in Python
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BioC
We describe a simple XML format to share text documents and annotation
... - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We are interested in reuse, and we focus on common NLP tasks that are broadly useful for textmining. -
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GNU Hyperbole: The Everyday Info Manager
Create hyperlinks just by dragging, find contacts instantly, ...
...People who get used to Hyperbole find it helps them so much that they prefer never to use Emacs without it. Hyperbole includes easy-to-use, powerful hypertextual button types and links that can be made without the need to learn a markup language. It also includes a hierarchical, record-based contact manager, a rapid window and frame control system and a powerful multi-level auto-numbered outliner. -
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Wizards of Magic
A fun game inspired by the classic Pong
A battle between two elemental wizards that will determine the most undeterred. ~ About ~ In order to learn more about programming and games development, this is my first digital game that I developed during my free time with my friends Yuri Soares and Caio Vinicius. Using Python programming language and Pygame framework. Comments and suggestions are welcome. Enjoy! *Bugfix: When trying to change the language, an error message may appear.
