Flexible solutions (up to 16 GPU per node) for every budget: GTX1080 / GTX1080Ti / Tesla P100 / Tesla K80 / Tesla M60 / DGX1

There are a variety of new Automated Machine Learning (AML) platforms emerging that led us recently to ask if we’d be automated and unemployed any time soon. In this article we’ll cover the “Professional AML tools”. They require that you be fluent in R or Python which means that Citizen Data Scientists won’t be using them. They also significantly enhance productivity and reduce the redundant and tedious work that’s part of model building.

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey’s learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.

Learn how to perform word embedding using the Word2Vec methodology. Generate word maps using TensorFlow and prepare for deep learning approaches to NLP.

Create a convolutional neural network in 11 lines in this Keras tutorial. Learn how to build deep learning networks super-fast using the Keras framework.

This article shares some of the best tensorflow GitHub projects. It covers image, text, audio and other types of machine learning projects.

This post and accompanying code demonstrate: how to use a memory-efficient generator in Keras for deep learning (classification or regression) to process millions of image files using hundreds of GB or more of disk space; how to use the same generator to efficiently implement a merged model.

At the useR! conference last month, Jim Hester gave a talk about two packages that provide a modern database interface for R. Those packages are the odbc package (developed by Jim and other members of the RStudio team), and the DBI package (developed by Kirill Müller with support from the R Consortium).

This post is a collection of best practices for using neural networks in Natural Language Processing. It will be updated periodically as new insights become available and in order to keep track of our evolving understanding of Deep Learning for NLP.

In this post, I want to visit use cases in machine learning where using deep learning does not really make sense as well as tackle preconceptions that I think prevent deep learning to be used effectively, especially for newcomers.

In this article, we’ll focus on the few main generalized approaches of text classifier algorithms and their use cases. Along with the high-level discussion, we offer a collection of hands-on tutorials.

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

Qifeng Chen and Vladlen Koltun

ICCV 2017

Paper: https://arxiv.org/abs/1707.09405

Code: https://github.com/CQFIO/PhotographicImageSynthesis