How does deep learning attain such impressive results? Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images. Deep learning applications are used in industries from automated driving to medical devices.

Dash presents its new chart library for the web. We like how the article goes in depth with how the dash library designed. They describe how the handled error cases and multiple integrations in a smart way. We found their comparison to Excel and the R ecosystem on point. One implementation challenge is that you need to maintain a flask application and have a ReactJS friendly front-end.

Wondering why Samsung still hasn't enabled Bixby voice features in English despite promising a launch in the spring? A spokesperson tells the Korea Herald that the company just doesn't have enough "big data" to make its AI-powered voice assistant available in languages besides Korean. It needs that extra knowledge to train Bixby's deep learning system, the Herald says. That's borne out by US beta testing: Samsung says there have been some'unsatisfactory' responses so far. An unnamed source for the Herald also claims that it's made worse by the geographical and linguistic barriers between Samsung's Korean headquarters and researchers in California.

In 2012, convolutional neural networks (CNN) - a type of artificial neural network blossomed on the Imagenet challenge (Image recognition challenge) in a way that no one had expected. It outperformed the runner-up by over 10% Top-5 accuracy. One of the main reasons this model performed well is attributed to the increase in the computation power. CNNs have been around since 1998. They were used to detect handwritten images back then.

So the data deal between Royal Free London NHS Foundation Trust and DeepMind "failed to comply with" the law. So says the Information Commissioner's Office (ICO), the UK regulator charged with upholding data protection rules. The deal, the ICO said, erred in several ways. Royal Free should have notified its patients before handing their data to DeepMind, giving them a chance to opt out.

We are very excited today to open-source Sony's neural network libraries, a software that helps the workflows of deep learning research, development and production. Neural networks are the core ingredients of deep learning models. Deep learning has first received huge attention in 2012, when an image classification model accomplished a great leap in image recognition, winning against other models with a large gap, in the ImageNet Large Scale Visual Recognition Challenge. Nowadays, deep learning is widely used in many applications as an essential tool, not only as a pattern recognition algorithm, but also as a tool capable of modeling black-box systems. The architectures of deep learning models vary at a wide range, in various aspects; from small to large, from feed-forward to recurrent, from unsupervised to supervised and so on.

Three-dimensional views of human stem cells derived from skin showing DNA (blue), the cell membrane (purple) and other structures in yellow. No two stem cells are identical, even if they are genetic clones. This stunning diversity is revealed today in an enormous publicly available online catalogue of 3D stem cell images. The visuals were produced using deep learning analyses and cell lines altered with the gene-editing tool CRISPR. And soon the portal will allow researchers to predict variations in cell layouts that may foreshadow cancer and other diseases.

Every time DeepMind publishes a new paper, there is frenzied media coverage around it. Often you will read phrases that are often misleading. This is not only misleading, but it also makes the everyday non PhD person intimidated. In this post I will go through the paper in an attempt to explain this new architecture in simple terms. You can find the original paper here.

Humans are social animals and language is our primary tool to communicate with the society. But, what if machines could understand our language and then act accordingly? Natural Language Processing (NLP) is the science of teaching machines how to understand the language we humans speak and write. We recently launched an NLP skill test on which a total of 817 people registered. This skill test was designed to test your knowledge of Natural Language Processing.

The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard distribution matching approaches exist that can be interpreted as the matching of weighted sums of moments, e.g. Maximum Mean Discrepancy (MMD), an explicit order-wise matching of higher order moments has not been considered before. We propose to match the higher order central moments of probability distributions by means of order-wise moment differences. Our model does not require computationally expensive distance and kernel matrix computations. We utilize the equivalent representation of probability distributions by moment sequences to define a new distance function, called Central Moment Discrepancy (CMD). We prove that CMD is a metric on the set of probability distributions on a compact interval. We further prove that convergence of probability distributions on compact intervals w.r.t. the new metric implies convergence in distribution of the respective random variables. We test our approach on two different benchmark data sets for object recognition (Office) and sentiment analysis of product reviews (Amazon reviews). CMD achieves a new state-of-the-art performance on most domain adaptation tasks of Office and outperforms networks trained with MMD, Variational Fair Autoencoders and Domain Adversarial Neural Networks on Amazon reviews. In addition, a post-hoc parameter sensitivity analysis shows that the new approach is stable w.r.t. parameter changes in a certain interval. The source code of the experiments is publicly available.