Train in R, run on Android: Image segmentation with torch

We train a model for image segmentation in R, using torch together with luz, its high-level interface. We then JIT-trace the model on example input, so as to obtain an optimized representation that can run with no R installed. Finally, we show the model being run on Android.

Related Articles

Introduction to Autoencoders

Table of Contents Introduction to Autoencoders What Are Autoencoders? How Autoencoders Achieve High-Quality Reconstructions? Revisiting the Story Types of Autoencoder Vanilla Autoencoder Convolutional Autoencoder (CAE) Denoising Autoencoder Sparse Autoencoder Variational Autoencoder (VAE) Sequence-to-Sequence Autoencoder What Are the Applications of Autoencoders?…
The post Introduction to Autoencoders appeared first on PyImageSearch.

PyMC Open Source Development

In this episode of Open Source Directions, we were joined by Thomas Wiecki once again who talked about the work being done with PyMC. PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.