Search Results for "language learning" - Page 29
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Showing 934 open source projects for "language learning"
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Deepvoice3_pytorch
PyTorch implementation of convolutional neural networks
An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. -
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Translation Funtime is a software designed to help translators have a fun time while translating. It can also be used to do bidirectional translations, which is a traditional language learning technique going back to the middle ages and beyond. FEATURES - Dictionaries for French, Russian, Arabic, Sanskrit, Japanese (more coming in future releases) - Fun tools not featured in other translation software - Recycle button to facilitate bidirectional translations This is an ALPHA release. There are some bugs.
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CFD Python
Sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes
CFD Python, a.k.a. the 12 steps to Navier-Stokes, is a practical module for learning the foundations of Computational Fluid Dynamics (CFD) by coding solutions to the basic partial differential equations that describe the physics of fluid flow. The module was part of a course taught by Prof. Lorena Barba between 2009 and 2013 in the Mechanical Engineering department at Boston University (Prof. Barba since moved to George Washington University). -
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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. -
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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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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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unfluff
Automatically extract body content (and other cool stuff) from HTML
unfluff is a Node.js library designed to automatically extract the main content from an HTML document — stripping away navigation bars, ads, footers and other boilerplate to leave you with the “body content”, metadata (title, author, date) and other useful fields. It’s a tool very much aimed at content-analysis, web scraping, building datasets, or repurposing article text for downstream processing (like machine-learning or summarization). The API is simple: you feed in raw HTML and it returns a structured object with the extracted text and other fields. It supports caching internal representations to speed up repeated extractions. While its language support is best for English, it is still widely used in web-content-processing pipelines. The repository notes some limitations (e.g., languages like Chinese/Arabic/Korean may not be well-supported). ... -
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KAREL 3D
Learning programming language for kids
This is learning programming language for children Karel-3D. By words from LightBot: "Get kids hooked on coding with minutes!" Created by Karel 3D from the 8-bit microcomputer PMD 85-2 in 1986. His later version of Karel the Robot in 3D, created first in the Slovak Republic. JavaScript variant include only one small HTML file tested and works on all devices with keyboard and full JavaScript support in internet browser, or alternative pre-compiled JAVA V8 .jar file with webEngine and JavaFX and code in string. ... -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
...By breaking down the model into understandable components, it serves as a practical reference for students and practitioners learning how deep learning models handle text beyond traditional bag-of-words approaches. -
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Cog
Bringing the power of the command line to chat
Cog brings the power of the command line to the place you collaborate with your team all the time, your chat window. Powerful access control means you can collaborate around even the most sensitive tasks with confidence. A focus on extensibility and adaptability means that you can respond quickly to the unexpected, without your team losing visibility. Use Cog to manage your infrastructure, support peer learning, and conduct collaborative research at the same time, right from chat. Cog is... -
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anaGo
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition
anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as named entity recognition (NER), part-of-speech tagging (POS tagging), semantic role labeling (SRL) and so on. Unlike traditional sequence labeling solver, anaGo doesn't need to define any language-dependent features. Thus, we can easily use anaGo for any language. In anaGo, the simplest type of model is the Sequence model. Sequence model includes... -
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ECDICT
Free English to Chinese Dictionary Database
ECDICT is a comprehensive English–Chinese dictionary dataset packaged for developers who need an offline, queryable lexicon for applications, NLP, or educational tools. It aggregates headwords, phonetics, parts of speech, translations, and example information into formats that are easy to integrate. The project provides multiple distribution forms—commonly SQLite/CSV/StarDict-style files—so you can choose the right storage and query approach for your app. Because it’s offline and local, it... -
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DeepLearn
Implementation of research papers on Deep Learning+ NLP+ CV in Python
Welcome to DeepLearn. This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning. -
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SLING
A natural language frame semantics parser
The aim of the SLING project is to learn to read and understand Wikipedia articles in many languages for the purpose of knowledge base completion, e.g. adding facts mentioned in Wikipedia (and other sources) to the Wikidata knowledge base. We use frame semantics as a common representation for both knowledge representation and document annotation. The SLING parser can be trained to produce frame semantic representations of text directly without any explicit intervening linguistic... -
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Modern C++ Tutorial
Modern C++ Tutorial: C++11/14/17/20 On the Fly
...Those C++ programmers who still insist on using traditional C++ (this book refers to C++98 and the previous C++ features as traditional C++) but have not been exposed to modern C++ are seeing things like Lambda expressions. When it comes to class new features, it will even reveal the feeling of wonder that "you are not learning the same language. A large number of features injected into traditional C++ make the entire C++ more like a modern language. Modern C++ not only enhances the usability of the C++ language itself, but automates the modification of keyword semantics. -
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EvalAI
Evaluating state of the art in AI
EvalAI is an open-source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. We allow the creation of an arbitrary number of evaluation phases and dataset splits, compatibility using any programming language, and organizing results in both public and private leaderboards. Certain large-scale challenges need special computing capabilities for evaluation. -
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Tensor Comprehensions
A domain specific language to express machine learning workloads
Tensor Comprehensions (TC) is a fully functional C++ library that automatically synthesizes high-performance machine learning kernels using Halide, ISL, and NVRTC or LLVM. TC additionally provides basic integration with Caffe2 and PyTorch. We provide more details in our paper on arXiv. This library is designed to be highly portable, machine-learning-framework agnostic and only requires a simple tensor library with memory allocation, offloading, and synchronization capabilities. -
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Keras resources
Directory of tutorials and open-source code repositories
Keras resources is a curated directory of tutorials, projects, and educational materials designed to help developers learn and work effectively with the Keras deep learning library. It aggregates a wide range of resources, including beginner guides, advanced tutorials, code examples, and third-party tools, all organized into a single reference hub. The repository covers diverse topics such as image classification, natural language processing, reinforcement learning, and generative models, providing both theoretical and practical insights. ... -
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MoodlePeers
[we moved to github! see link]
Learning group formation by manifold homogeneous and heterogeneous to match criteria - will be provided by plugins for the learning management system Moodle. Other accompanying plugins will be usable basedon Moodle groups: - provide learning analytics for learning goals (individually visualized in comparison to group and course) - group tasks to be performed collaboratively Language support: primarily English and German. -
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OpenPR stands for Open Pattern Recognition project and is intended to be an open source library for algorithms of image processing, computer vision, natural language processing, pattern recognition, machine learning and the related fields.
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Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It... -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract... -
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anpe
anpe is Ada Neural Prolog Engine
anpe is clients/server Ada/Prolog system for natural language traitement with Prolog Engine and Artificial Neural Network with Ada. -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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House3D
A Realistic and Rich 3D Environment
House3D is a large-scale virtual 3D simulation environment designed to support research in embodied AI, reinforcement learning, and vision-language navigation. It provides more than 45,000 richly annotated indoor scenes sourced from the SUNCG dataset, covering diverse architectural layouts such as studios, multi-floor homes, and spaces with detailed furnishings and room types. Each environment includes fully labeled 3D objects, allowing agents to perceive and interact with their surroundings through multiple sensory modalities including RGB images, depth maps, semantic segmentation masks, and top-down maps. ...
