Search Results for "learning" - Page 11
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Showing 301 open source projects for "learning"
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Shoplogix Smart Factory PlatformReal-time Visibility into Your Shop Floor's Performance. The Shoplogix smart factory platform enables manufacturers to increase overall equipment effectiveness, reduce operational costs, sustain growth and improve profitability by allowing them to visualize, integrate and act on production and machine performance in real-time. Manufacturers that trust us to drive efficiency in their factories. Real-time visual data and analytics provide valuable insights to make better informed decisions. Uncover hidden shop floor potential and drive rapid time to value. Develop a continuously improving culture through training, education and data-driven decisions. Compete in the i4.0 world by making the Shoplogix Smart Factory Platform the cornerstone of your digital transformation. Connect to any equipment or device to automate data collection and exchange it with other manufacturing technologies. Automatically monitor, report and analyze machine states to track real-time production.
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Build experiences that drive engagement and increase transactionsSendbird's chat, voice, and video APIs power conversations and communities in hundreds of the most innovative apps and products. Sendbird’s feature-rich platform, and pre-fab UI components make developers more productive. We take care of a ton of operational complexity under the hood, so you can power a rich chat service, and life-like voice, and video experiences, and not worry about features, edge cases, reliability, or scale.
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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. ... -
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Pipelines
An experimental programming language for data flow
...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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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). ... -
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Data Algorithm/leetcode/lintcode
Data Structure and Algorithm notes
This work is some notes of learning and practicing data structures and algorithms. Part I is a brief introduction of basic data structures and algorithms, such as, linked lists, stack, queues, trees, sorting and etc. This book notes about learning data structure and algorithms. It was written in Simplified Chinese but other languages such as English and Traditional Chinese are also working in progress. -
Planfix: Manage Projects, Team's Tasks and Business ProcessesPlanfix is like a souped-up business process management system for folks who really know their stuff. It's built to help you dive deeper and gives you more options than your run-of-the-mill project and task management systems. Best part? Even small businesses and non-profits can get in on the action.
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virgo
32 bit VIRGO Linux Kernel
Linux kernel fork-off with cloud and machine learning features -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
...The project includes scripts for training, evaluation, and data handling, making it easy to run experiments on datasets such as movie reviews or other labeled text collections. 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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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. Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. ... -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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Zhao
A compilation of "The Princely Party Relationship Network"
zhao is a repository that consolidates research, data, and insights related to Zhao, which is likely an individual’s research collection, notes, or curated resources on deep learning, AI, or computational topics (name and content context suggest specialized study). The project may include code examples, experiment results, references to academic papers, mathematical notes, and supporting scripts to explore specific ML methods, benchmarks, or theoretical findings. Because it aggregates content associated with Zhao, the repository functions as a personal or shared knowledge base for readers who want insight into a body of research rather than a traditional software library. ... -
Awardco Employee RecognitionEverything you love about Amazon is now available for rewards and recognition. Awardco has partnered with Amazon Business to bring millions of reward choices, lower vendor fees and dollar-for-dollar recognition spend to your organization. More choice, more capability, and less spend - all in one simple platform.
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. ... -
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Tangent
Source-to-source debuggable derivatives in pure Python
...In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy. -
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cnn-benchmarks
Benchmarks for popular CNN models
...Overall, cnn-benchmarks is a practical tool for performance analysis in deep learning workflows. -
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A Complete Beginner's Guide to Django
A Complete Beginner's Guide to Django - Code Samples
Code samples from the Django tutorial series. I’m starting a new tutorial series about Django fundamentals. It’s a complete beginner’s guide to start learning Django. The material is divided into seven parts. We’re going to explore all the basic concepts in great detail, from installation, and preparation of the development environment, models, views, templates, URLs to more advanced topics such as migrations, testing, and deployment. I wanted to do something different. A tutorial that would be easy to follow, informative and fun to read. ... -
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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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Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
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itchat
A complete and graceful API for Wechat
...Of course, the use of this api is far more than a robot, and more functions are waiting for you to discover. This interface shares a similar operation method with the public account interface itchatmp , learning to master two tools at a time. Now WeChat has become a large part of personal social networking. I hope this project can help you expand your personal WeChat account and facilitate your life. By printing the chat user and the parameters of the registration message, you can find that these values are all dictionaries. But in fact, that has carefully constructed corresponding messages, users, group chats, and official accounts. -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
...The explanations are present in the wiki associated with this repository. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. TensorFlow is designed to facilitate this goal. ... -
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Ezhil-Lang
தமிழில் கணினி மொழி
எழில் - ஒரு தமிழ் நிரலாக்க மொழி; தமிழ் மாணவர்களுக்கு இது முதல்முறை கணி Ezhil is a Tamil script based programming language for children and teens in the K-12 grade schools. Ezhil enables learning imperative programming like BASIC or LOGO in Tamil language. -
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seq2seq
A general-purpose encoder-decoder framework for Tensorflow
seq2seq is an early, influential TensorFlow reference implementation for sequence-to-sequence learning with attention, covering tasks like neural machine translation, summarization, and dialogue. It packaged encoders, decoders, attention mechanisms, and beam search into a modular training and inference framework. The codebase showcased best practices for batching, bucketing by sequence length, and handling variable-length sequences efficiently on GPUs. -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
...The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible. -
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PrettyTensor
Pretty Tensor: Fluent Networks in TensorFlow
Pretty Tensor is a high-level API built on top of TensorFlow that simplifies the process of creating and managing deep learning models. It wraps TensorFlow tensors in a chainable object syntax, allowing developers to build multi-layer neural networks with concise and readable code. Pretty Tensor preserves full compatibility with TensorFlow’s core functionality while providing syntactic sugar for defining complex architectures such as convolutional and recurrent networks. -
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Algorithms in Python
Data Structures and Algorithms in Python
...Because it’s openly maintained, you can browse through issues, see test cases, and observe coding style in a “learning through code” fashion. It also serves as a playground where you can add problems, measure performance, and compare different algorithmic approaches. For anyone striving to move from “I know the syntax” to “I know how to use the right algorithm at the right time,” this repository is a practical asset. -
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Question Answering Corpus
Question answering dataset in "Teaching Machines to Read & Comprehend"
...The repository provides scripts for downloading archived CNN and Daily Mail articles from the Wayback Machine and automatically generating cloze-style questions where entities in the text are replaced with placeholders. Each data instance consists of a news article (context), a generated question, and its corresponding answer, making it suitable for supervised machine learning setups. The output follows a standardized question-answer format, with entity mappings to help models resolve named references.
