Search Results for "rapid" - Page 4

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 ship them at scale. ...

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 this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. ...

...By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models. Spotlight offers a slew of popular datasets, including Movielens 100K, 1M, 10M, and 20M. It also incorporates utilities for creating synthetic datasets. For example, generate_sequential generates a Markov-chain-derived interaction dataset, where the next item a user chooses is a function of their previous interactions. ...

OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as...

...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. The strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model.

RoboComp is a robotics framework providing a set of open-source, distributed, real-time robotic and artificial vision software components and the necessary tools to create and manage them.

Onyx is for rapid prototyping and large-scale experimentation on advanced machine-learning algorithms with an emphasis on algorithms for online or streaming analysis, modeling, and classification.

This project is developing a modular framework for constructing machine intelligences. The modular approach allows for both rapid development of simple AI's and also construction of larger and more general learning based engines.

An AI sandbox for learning game programming. It includes an integrated development environment, and functionality for rapid development of virtual characters.

Betelgeuse is a machine learning modeling package designed to meet the requirements of heavy-duty industry use. It was designed to be efficient, reliable, and highly modular; it is developed primarily in Python to promote maintainability and rapid development, but uses Cython and C in critical bottlenecks for efficiency. It focuses on high-quality implementations of a diverse set of the most widely used machine learning algorithms. An important goal of Betelgeuse is to have a clean, professional user interface amenable to less technical users, and to have multiple user interfaces for graphical, command line, and remote server use.

jrgp is a strong-typed Genetic Programming system, which features a graphical interface (gool) to setup and run GP-problems and a tool (fs-d) that greatly simplifies the definition of a GP-problem.

...With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model.