Search Results for "model train"

LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle...

TensorFlow Lite for Microcontrollers is a TensorFlow Lite runtime designed for running machine learning models on tiny embedded devices. It targets microcontrollers, DSPs, and other resource-constrained hardware where memory, compute, and power are limited. The project enables on-device inference without depending on an operating system, standard C or C++ libraries, or dynamic memory allocation. It is useful for applications such as wake-word detection, sensor analysis, gesture recognition,...

In the beginning, machines learned in darkness, and data scientists struggled in the void to explain them. InterpretML is an open-source package that incorporates state-of-the-art machine-learning interpretability techniques under one roof. With this package, you can train interpretable glass box models and explain black box systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.

First, install Flashlight (using the 0.3 branch is required) with the ASR application. This repository includes recipes to reproduce the following research papers as well as pre-trained models. All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run...

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine...