Search Results for "language learning" - Page 27
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Showing 934 open source projects for "language learning"
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Chastity Checkerboard
Drawing Checkerboards with the C Programming Language
There are multple files in this project and they contain my very best code that has been improved slowly for over a decade as I've found the time to work on my programming. By combining my experience with the C programming language and learning to use the SDL library starting in 2019, I managed to get a checkerboard on my computer screen. Since then I slowly removed all SDL dependent code and have learned to write the bytes of image file formats such as PBM,BMP, and even TIFF! I also invented my own image format which will be part of it's own project soon. ... -
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PSKoans
A simple, fun, and interactive way to learn the PowerShell language
PSKoans is an interactive, educational PowerShell module that teaches core PowerShell concepts via Pester test-driven “koans” (small exercises that initially fail and must be fixed). Designed for beginners to learn PowerShell through hands‑on problem solving, it guides users through basic syntax, data structures, and automation tasks. -
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PyText
A natural language modeling framework based on PyTorch
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly... -
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Essential Netty in Action
It is a book about the Essentials of Norman Maurer's Netty in Action
...Readers can browse the Markdown source, use the GitBook edition, or read a separately published website version. Because it reflects an older Netty era, it is most useful as a foundational and historical learning resource. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. -
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
...The framework provides code for both supervised learning (training from human dialogue data) and reinforcement learning (via self-play and rollout-based planning). It introduces a hierarchical latent model, where high-level intents are first clustered and then translated into coherent language, improving dialogue diversity and goal consistency. The repository also includes the Negotiate dataset, comprising over 5,800 dialogues across 2,200 unique scenarios. -
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Flutter Example Apps
Basic Flutter apps, for flutter devs
...This repository is frequently starred and forked by developers because it provides a broad, hands-on showcase of how different features of the Flutter framework and Dart language are used in practice. Each example typically links to online resources or videos that walk through how the app was built, making it both a reference collection and a learning tool for beginners and intermediates alike. -
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Smart Algorithm
Repository implementing a variety of intelligent algorithms
...The implementations are provided in multiple languages (Java, Python, MATLAB). The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. Particle Swarm Optimization (PSO) implementations in multiple languages. Immune Algorithm (or immune-inspired optimization) implementations. Multiple versions/language compatibility (Java, Python, MATLAB). -
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Python Crawler Tutorial Starts From Zero
Python crawler tutorial, taking you from zero to one
Python Crawler Tutorial Starts From Zero is a Chinese-language learning repository that teaches web crawling from introductory concepts through practical examples. Early lessons explain HTTP requests, request analysis, the Python Requests library, and common categories of extracted data. Separate chapters cover JSON processing and regular expressions for transforming responses into structured information. -
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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. -
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Judge0 IDE
Simple, free and open-source online code editor
...It's perfect for anybody who just wants to quickly write and run some code without opening a full-featured IDE on their computer. Moreover, it is also useful for teaching and learning or just trying out a new language. -
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JS Bits
JavaScript concepts with code
JS Bits is an open-source collection of JavaScript concepts explained through concise code examples and clear explanations that serve as a practical learning resource for both beginners and experienced developers. It breaks down essential parts of the language — such as core syntax, quirky behaviors, functions, scope, closures, asynchronous patterns, and commonly misunderstood features — into digestible pieces that make it easier to internalize how JavaScript really works in modern development. ... -
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fastText
Library for fast text classification and representation
...The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels). -
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Curso Grátis de Git e GitHub
Free Git and Github course material
...The repository contains downloadable PDF manuals for reference and review. It also includes classroom slide decks that follow the structure of the lessons. Students can clone or browse the repository while learning how Git repositories and GitHub collaboration work. The materials are especially suited to beginners who want structured Portuguese-language guidance alongside practical repository use. An MIT license allows the educational files to be reused and adapted within its terms. -
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NLP-progress
Repository to track the progress in Natural Language Processing (NLP)
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. This document aims to track the progress in Natural Language Processing (NLP) and give an overview of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets. It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such... -
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jk
Configuration as Code with ECMAScript
jk is pre-1.0, and as such, features, commands, and APIs inevitably will change. jk is a data templating tool designed to help write structured configuration files. The main idea behind jk is to use a general-purpose language for this task. They offer mature tooling, great runtimes, a well-established ecosystem, and many learning resources. jk uses Javascript and a runtime tailored for configuration. -
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Top deep learning Github repositories
Top 200 deep learning Github repositories sorted by stars
...The repository categorizes projects related to neural networks, computer vision, natural language processing, reinforcement learning, and other areas of artificial intelligence. By collecting popular open-source implementations in one place, the project simplifies the process of exploring cutting-edge tools and research implementations for deep learning practitioners. The curated lists are particularly helpful for developers who want to quickly identify well-maintained projects with strong community support. -
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Weld
High-performance runtime for data analytics applications
...Weld is particularly useful for workloads involving large-scale data processing in frameworks such as NumPy, Spark, and TensorFlow. The language includes built-in constructs for expressing data-parallel operations, enabling efficient execution on modern hardware architectures. By combining operations from multiple libraries into a single optimized execution plan, Weld can significantly improve performance in analytics and machine learning pipelines. -
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Deep Learning Drizzle
Drench yourself in Deep Learning, Reinforcement Learning
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures! Optimization courses which form the foundation for ML, DL, RL. Computer Vision courses which are DL & ML heavy. Speech recognition courses which are DL heavy. Structured Courses on Geometric, Graph Neural Networks. Section on Autonomous Vehicles. Section on Computer Graphics with ML/DL focus. -
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cocoNLP
A Chinese information extraction tool
...Because it aims at utility over complexity, it’s useful for prototyping data products or building lightweight text analytics where large models would be overkill. The repository also includes examples and test snippets to help you understand expected inputs and typical outputs, which shortens the learning curve for newcomers. -
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python-is-cool
Cool Python features for machine learning
python-is-cool is an educational repository created by AI researcher Chip Huyen that explores lesser-known features and powerful capabilities of the Python programming language. The project serves as a curated guide to Python techniques that many developers may overlook or hesitate to use due to unfamiliarity. It demonstrates practical language features, idiomatic programming patterns, and useful tricks that can make Python code more expressive, efficient, and elegant. Much of the content is... -
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PRMLT
Matlab code of machine learning algorithms in book PRML
This Matlab package implements machine learning algorithms described in the great textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML). It is written purely in Matlab language. It is self-contained. There is no external dependency. This package requires Matlab R2016b or latter, since it utilizes a new Matlab syntax called Implicit expansion (a.k.a. broadcasting). -
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Texar
Toolkit for Machine Learning, Natural Language Processing
Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. -
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
PyTorch-NLP is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out...
