Search Results for "intent driven network"

...However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock prices is a complex task, as it is influenced by various factors such as market trends, political events, and economic indicators. The fluctuations in stock prices are driven by the forces of supply and demand, which can be unpredictable at times. ...

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...

smartNode is a visual simulation platform for space-based intelligent relay and satellite data-return scenarios. It models the relationship between satellites, ground stations, relay links, and content-driven task scheduling. The project includes a Python backend and a browser-based frontend, making it suitable for local simulation, teaching, and secondary development. Users can view a three-dimensional space situation, submit data return tasks, and monitor resource states in real time. The system exposes APIs for health checks, simulation data, resource status, utilization metrics, and configuration updates. smartNode is best suited for aerospace students, communications learners, instructors, and developers exploring space-based network simulation.

compare_gan is a research codebase that standardizes how Generative Adversarial Networks are trained and evaluated so results are comparable across papers and datasets. It offers reference implementations for popular GAN architectures and losses, plus a consistent training harness to remove confounding differences in optimization or preprocessing. The library’s evaluation suite includes widely used metrics and diagnostics that quantify sample quality, diversity, and mode coverage. With...