Behavior Tree Starter Kit
The main features of aigamedev/btsk are: AI and Machine Learning, Artificial Intelligence, Machine Learning and AI.
Open-source alternatives to aigamedev/btsk include: tensorflow/tensorflow — TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of… dobiasd/frugally-deep — A lightweight header-only library for using Keras (TensorFlow) models in C++. annetgpgpu/annetgpgpu — A GPU (CUDA) based Artificial Neural Network library. apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… codeplea/genann — simple neural network library in ANSI C. bayeswitnesses/m2cgen — Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart,…
TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
A GPU (CUDA) based Artificial Neural Network library
Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap