Search Results for "ai coding model"

This package implements interpretability methods for black box models, with a focus on local explanations and attribution maps in input space. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Most of the implemented methods only require the model to be differentiable with Zygote. Layerwise Relevance Propagation (LRP) is implemented for use with Flux.jl models.

PromptingTools.jl is a Julia-based toolkit designed to simplify prompt engineering and unify interactions with multiple large language model providers through a consistent interface. It focuses on reducing the complexity of prompt creation by introducing templating systems, macros, and reusable functions that standardize how prompts are constructed and executed. The library provides a family of ai* functions that handle tasks such as generation, embeddings, classification, and data extraction, all following a consistent structure. ...

Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent of coding a (log) posterior density in Julia. ...

The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic.

StatsWithJuliaBook is the companion code repository for the book Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. It contains over 200 code blocks that correspond to the book’s ten chapters and three appendices, covering topics from probability theory and data summarization to regression analysis, hypothesis testing, and machine learning basics. The repository is designed for Julia users and provides ready-to-run examples that reinforce...