Search Results for "deep learning ai"

Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. ...

Triton is a programming language and compiler framework specifically designed for writing highly efficient custom deep learning operations, particularly for GPUs. It aims to bridge the gap between low-level GPU programming, such as CUDA, and higher-level abstractions by providing a more productive and flexible environment for developers. Triton enables users to write optimized kernels for machine learning workloads while maintaining readability and control over performance-critical aspects like memory access patterns and parallel execution. ...

Pyro is a flexible, universal probabilistic programming language (PPL) built on PyTorch. It allows for expressive deep probabilistic modeling, combining the best of modern deep learning and Bayesian modeling. Pyro is centered on four main principles: Universal, Scalable, Minimal and Flexible. Pyro is universal in that it can represent any computable probability distribution. It scales easily to large datasets with minimal overhead, and has a small yet powerful core of composable abstractions that make it both agile and maintainable. ...

...Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. ...