Search Results for "construct" - Page 2

...The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. It is written in a way that you can extend it yourself or deviate from the implemented convenience methods and construct your own complex experiments or algorithms.

Recent advances in machine learning have created opportunities for “AI” technologies to assist unlocking creativity in powerful ways. PyTTI is a toolkit that facilitates image generation, animation, and manipulation using processes that could be thought of as a human artist collaborating with AI assistants. The underlying technology is complex, but you don’t need to be a deep learning expert or even know coding of any kind to use these tools. Understanding the underlying technology can be...

The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related...

...Accelerated Text is a natural language generation tool which allows you to define data descriptions and then generates multiple versions of those descriptions varying in wording and structure. Accelerated Text is a no-code natural language generation platform. It will help you construct document plans which define how your data is converted to textual descriptions. With Accelerated Text you can use such data to generate text for your business reports, your e-commerce platform or your customer support system. Data descriptions require precision. Accelerated Text follows the principle of this strict adherence to data-bound text generation. ...

...The repository provides environment generation code that builds on the mujoco-worldgen package, enabling dynamic creation of simulated physical environments. Developers can construct custom environments by combining modular components such as Boxes, Ramps, and RandomWalls using a flexible layering approach that reduces code duplication. The framework includes several predefined environments—such as Hide and Seek, Box Locking, Blueprint Construction, and Shelter Construction—that model distinct problem-solving and collaboration scenarios.

...Many examples are structured as standalone scripts or notebooks that can be executed directly to reproduce the results described in the book. The code demonstrates how TensorFlow can be used to construct training pipelines, prepare datasets, and evaluate model performance.

Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. ...

...The algorithms included in TreeLiker are unique in that, in principle, they are able to search given sets of relational patterns exhaustively, thus guaranteeing that if some good pattern capturing an important feature of the problem exists, it will be found. In experiments with real-life data, the algorithms were shown to be able to construct complete non-redundant sets of patterns for chemical datasets involving several thousands of molecules as well as for comparably large datasets from genomics or proteomics. The included relational learning algorithms are tailored towards so-called tree-like features for which some otherwise very hard sub-problems (NP-hard) become tractable.

SNTOOL (Semionic Network Toolkit) is a computational framework to construct DES (Discrete Event Systems) and Intelligent Systems. It provides a full-featured graphical user interface to design and simulate models.