Search Results for "example"

Kener: Open-source Node.js status page tool, designed to make service monitoring and incident handling a breeze. It offers a sleek and user-friendly interface that simplifies tracking service outages and improves how we communicate during incidents. And the best part? Kener integrates seamlessly with GitHub, making incident management a team effort—making it easier for us to track and fix issues together in a collaborative and friendly environment.

Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural...

...Powered by leading cloud and offline/local AI models including Google Gemini, OpenAI, xAI Grok, DeepL, Azure, and Ollama (Gemma, Qwen), it seamlessly translates 240+ languages with an intuitive, plug-and-play interface. Example: They speak German → you see Turkish They speak Turkish → you see German 🧠 AI Model Support Google Gemini: 2.5 Flash, 2.5 Pro OpenAI: GPT-4o, GPT-4 Turbo xAI: Grok Local/Offline (via Ollama Engine): Gemma, Qwen

"Next Generation Programming - Programming Without Coding Software" is a drag-drop wizard for creating simple or complex applications without writing any programming language code The Software is coded/designed with "Java Programming Language" for novice/expert programmers; Programmers can write softwares with visual tools : drag-drop components;visual editors... Programmers can use the software to compose of simple/complex applications : Database programs, circuit design, generate...

...You will find here some links to more or less popular github repos, libraries, papers, and other information. Data augmentation can be simply described as any method that makes our dataset larger. To create more images for example, we could zoom in and save a result, we could change the brightness of the image or rotate it. To get a bigger sound dataset we could try to raise or lower the pitch of the audio sample or slow down/speed up. Keypoints/landmarks Augmentation, usually done with image augmentation (rotation, reflection) or graph augmentation methods (node/edge dropping) Spectrograms/Melspectrograms, usually done with time series data augmentation (jittering, perturbing, warping) or image augmentation (random erasing)

...Gluon NLP makes it easy to evaluate and train word embeddings. Here are examples to evaluate the pre-trained embeddings included in the Gluon NLP toolkit as well as example scripts for training embeddings on custom datasets. Fasttext models trained with the library of Facebook research are exported both in text and a binary format. Unlike the text format, the binary format preserves information about subword units and consequently supports the computation of word vectors for words unknown during training (and not included in the text format). ...

...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 this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. Wrap any code that's random, with fork_rng and you'll be good to go. Now that you've computed your vocabulary, you may want to make use of pre-trained word vectors to set your embeddings.

Free and open source grammar checker. Currently capable of identifying errors: 1) Incomplete sentence (fragment) 2) Subject Verb Plurality Agreement Incomplete Sentence Example: "John a very man." Subject Verb Plurality Agreement Example: "They walks into a classroom." This software utilizes parts-of-speech tagging software that was developed and published by the Natural Language Processing Group at Stanford University. Many thanks!!! (Full citation in README)

...For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.

Maximum entropy is a powerful method for constructing statistical models of classification tasks, such as part of speech tagging in Natural Language Processing. Several example applications using maxent can be found in the OpenNLP Tools Library.

NXTAI's main objective is to create a new generation of natural language processing program by using a unique neural networking model. To demonstrate the algorithm works, A chatbot will be developed as a classical example of how it can be used.