Search Results for "seismic data processing software"
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Showing 7 open source projects for "seismic data processing software"
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with... -
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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of... -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a... -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution...
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