Search Results for "parallel-sudoku-solver" - Page 7
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Showing 312 open source projects for "parallel-sudoku-solver"
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Parakeet
PAddle PARAllel text-to-speech toolKIT
PAddle PARAllel text-to-speech toolKIT (supporting Tacotron2, Transformer TTS, FastSpeech2/FastPitch, SpeedySpeech, WaveFlow and Parallel WaveGAN) Parakeet aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It is built on PaddlePaddle dynamic graph and includes many influential TTS models. -
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pymp - Python music player Start flags: -l --loop -v --volume <volume> -p --parallel <file1> <file2>
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scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
scikit-opt is a Python library for solving optimization problems with evolutionary and swarm-intelligence algorithms. It includes genetic algorithms, particle swarm optimization, differential evolution, simulated annealing, ant colony optimization, immune algorithms, and artificial fish swarms. The package can address continuous objectives, constrained problems, and combinatorial tasks such as the traveling salesman problem. A consistent workflow lets users define an objective, configure an... -
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VITS
Conditional Variational Autoencoder with Adversarial Learning
...Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation (fast inference) while achieving speech quality that rivals or surpasses many two-stage systems. The repository provides training and inference pipelines for common datasets such as LJ Speech (single-speaker) and VCTK (multi-speaker), including filelists, configs, and preprocessing scripts. It also includes monotonic alignment search code and g2p preprocessing, which are crucial components for aligning text and speech in an end-to-end setup. -
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Transformer TTS
Implementation of a Transformer based neural network
...This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. The repository ships with tooling to build datasets (especially LJSpeech) and create training data, plus scripts to train both the aligner and the TTS model, monitor training with TensorBoard, and resume or reset training runs. -
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FARM
Fast & easy transfer learning for NLP
...With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. Integration of custom datasets via Processor class. Powerful experiment tracking & execution. -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
XLM (Cross-lingual Language Model) is a family of multilingual pretraining methods that align representations across languages to enable strong zero-shot transfer. It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment. Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence labeling tasks such as XNLI, NER, and POS without target-language supervision. The repository provides preprocessing pipelines, training code, and fine-tuning scripts so you can reproduce benchmark results or adapt models to your own multilingual corpora. ... -
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The Development System for Auditory Modelling (DSAM) is a computational library designed specifically for producing simulations of the auditory system. It brings together many established auditory models within a flexible programming platform.
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...Comprehensive reference help is available at http://minimpy.sourceforge.net MinimPy has been fully described in the following article: Saghaei, M. and Saghaei, S. (2011) Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials. Journal of Biomedical Science and Engineering, 4, 734-739. doi: 10.4236/jbise.2011.411090. Available at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=8518
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lzhw
LZHW Windows command line lossless compression tool for tabular files
LZHW Command Line Lossless Compression Tool is a Windows command line tool used to compress and decompress files from and to any form, csv, excel etc without any dependencies or installations. Using an optimized algorithm (LZHW) developed from Lempel-Ziv, Huffman and LZ-Welch algorithms. The tool can work in parallel and most of its code is written in Cython, so it is pretty fast. It is based on python lzhw library. Full tool documentation can be found at: https://mnoorfawi.github.io/lzhw/6%20Using%20the%20lzhw%20command%20line%20tool/ While the documentation for the python library is at: https://mnoorfawi.github.io/lzhw/ -
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Frontend Regression Validator (FRED)
Visual regression tool used to compare baseline and updated instances
...The visual analysis computes the Normalized Mean Squared error and the Structural Similarity Index on the screenshots of the baseline and updated sites, while the visual AI looks at layout and content changes independently by applying image segmentation Machine Learning techniques to recognize high-level text and image visual structures. This reduces the impact of dynamic content yielding false positives. FRED is designed to be scalable. It has an internal queue and can process websites in parallel depending on the amount of RAM and CPUs (or GPUs) available. -
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DETR
End-to-end object detection with transformers
...Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient. -
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GoodByeCatpcha
Solver ReCaptcha v2 Free
An async Python library to automate solving ReCAPTCHA v2 by images/audio using Mozilla's DeepSpeech, PocketSphinx, Microsoft Azure’s, Google Speech and Amazon's Transcribe Speech-to-Text API. Also image recognition to detect the object suggested in the captcha. Built with Pyppeteer for Chrome automation framework and similarities to Puppeteer, PyDub for easily converting MP3 files into WAV, aiohttp for async minimalistic web-server, and Python’s built-in AsyncIO for convenience. -
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Zat 1.0
Fast N-SAT solver for Windows 10
Zat is an Onagan project. It is a very fast sat solver for .cnf, .dimacs, ... Zat is an O(n^log(n)) time complexity algorithm, which is faster than O((1+e)^n) for e strictly positive. The space complexity is O(n). The version 1.0 is exclusively for Windows 10. -
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APBS
Biomolecular electrostatics software
This software has moved to http://www.poissonboltzmann.org/. -
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The TRANSIMS Studio application is an integrated development environment for the TRansportation ANalysis and SIMulation System (TRANSIMS). Components include a run time environment to execute TRANSIMS in parallel, as well as a full featured GUI.
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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pipupgrade
Like yarn outdated/upgrade, but for pip
...By analyzing semantic versioning, pipupgrade categorizes updates into major, minor, and patch changes, allowing developers to make informed decisions about which packages to upgrade. Additionally, it supports updating requirements.txt and Pipfile files, ensuring that dependency specifications remain current. With features like parallel processing and dependency graph visualization, pipupgrade enhances the efficiency and clarity of Python package maintenance. -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. ... -
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imaplib2 module
Threaded Python IMAP4 client
Python IMAP4rev1 mail protocol client class using threads for parallel operation. PLEASE NOTE - active development of this module has now moved to https://github.com/imaplib2/imaplib2 -
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MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
MUSE is a framework for learning multilingual word embeddings that live in a shared space, enabling bilingual lexicon induction, cross-lingual retrieval, and zero-shot transfer. It supports both supervised alignment with seed dictionaries and unsupervised alignment that starts without parallel data by using adversarial initialization followed by Procrustes refinement. The code can align pre-trained monolingual embeddings (such as fastText) across dozens of languages and provides standardized evaluation scripts and dictionaries. By mapping languages into a common vector space, MUSE makes it straightforward to build cross-lingual applications where resources are scarce for some languages. ... -
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porttree
Show dependences of a FreeBSD port as a pseudo graphic tree
For a given FreeBSD port, determine its dependences using any combination of the FETCH_, EXTRACT_, PATCH_, BUILD_, LIB_, RUN_ and TEST_DEPENDS, and show them as a pseudo graphical tree. Use back references for cross-connections. -
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Scalable Distributed Deep-RL
A TensorFlow implementation of Scalable Distributed Deep-RL
...IMPALA introduced a new paradigm for efficiently training agents across large-scale environments by decoupling acting and learning processes. In this architecture, multiple actor processes interact with their environments in parallel to collect trajectories, which are then asynchronously sent to a centralized learner for policy updates. The learner uses importance weighting to correct for policy lag between actors and the learner, enabling stable off-policy training at scale. This design allows the system to scale efficiently to hundreds of environments and billions of frames while maintaining sample efficiency and stability. ... -
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Pipelines
An experimental programming language for data flow
Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires the implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline while providing access to thousands of active libraries for machine learning, data analysis, and processing. -
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Sudoku-Tk
Sudoku games and puzzle solver
Play and solve sudoku puzzles. Start from an empty grid, generate a puzzle or load one. The game is timed. The generated puzzles have a unique solution. Mistakes (twice the same number on the line / column / row) are highlighted in red.
