Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. FNNs that perform well are typically shallow and, therefore cannot exploit many levels of abstract representations. We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations. While batch normalization requires explicit normalization, neuron activations of SNNs automatically converge towards zero mean and unit variance. The activation function of SNNs are "scaled exponential linear units" (SELUs), which induce self-normalizing properties. Using the Banach fixed-point theorem, we prove that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero mean and unit variance -- even under the presence of noise and perturbations. This convergence property of SNNs allows to (1) train deep networks with many layers, (2) employ strong regularization, and (3) to make learning highly robust. Furthermore, for activations not close to unit variance, we prove an upper and lower bound on the variance, thus, vanishing and exploding gradients are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with standard FNNs and other machine learning methods such as random forests and support vector machines. SNNs significantly outperformed all competing FNN methods at 121 UCI tasks, outperformed all competing methods at the Tox21 dataset, and set a new record at an astronomy data set. The winning SNN architectures are often very deep. Implementations are available at: github.com/bioinf-jku/SNNs.

We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.

Artificial intelligence is giving rise to unprecedented capabilities for surveillance, from facial recognition at bridge crossings to the ability to identify thousands of people at once. Now, new research suggests that AI could potentially be used to identify people who have taken steps to conceal their identities by wearing hats, sunglasses, or scarves over their faces. The paper, accepted to appear in a computer vision conference workshop next month and detailed in Jack Clark's ImportAI newsletter, shows that identifying people covering their faces is possible, but there's a long way to go before it's accurate enough to be relied upon. Researchers used a deep-learning algorithm--a flavor of artificial intelligence that detects patterns within massive amounts of data--to find specific points on a person's face and analyze the distance between those points. When asked to compare a face concealed by a hat or scarf against photos of five people, the algorithm was able to correctly identify the person 56% of the time.

Building a model in Keras starts by constructing an empty Sequential model. The result of Sequential, as with most of the functions provided by kerasR, is a python.builtin.object. This object type, defined from the reticulate package, provides direct access to all of the methods and attributes exposed by the underlying python class. To access these, we use the $ operator followed by the method name. Layers are added by calling the method add.

Protesters regularly wear disguises like bandanas and sunglasses to prevent being identified, either by law enforcement or internet sleuths. Their efforts may be no match for artificial intelligence, however. A new paper to be presented at the IEEE International Conference on Computer Vision Workshops (ICCVW) introduces a deep-learning algorithm--a subset of machine learning used to detect and model patterns in large heaps of data--that can identify an individual even when part of their face is obscured. The system was able to correctly identify a person concealed by a scarf 67 percent of the time when they were photographed against a "complex" background, which better resembles real-world conditions. The deep-learning algorithm works in a novel way.

"NLP is booming", declared Joakim Nivre at the presidential address of ACL 2017, which I attended in Vancouver earlier this month. As evidenced by the throngs of attendees, interest in NLP is at an all-time high – an increase that is chiefly due to the successes of the deep learning renaissance, which recently swept like a tidal wave over the field. Beneath the optimism however, I noticed a tangible anxiety at ACL, as one field adjusts to its rapid transformation by another. Researchers asked whether there is anything of the old NLP left – or was it all swept away by the tidal wave? Are neural networks the only technique we need any more?

Would you like to take a course on Keras and deep learning in Python? Consider taking DataCamp's Deep Learning in Python course! Also, don't miss our Keras cheat sheet, which shows you the six steps that you need to go through to build neural networks in Python with code examples! Before going deeper into Keras and how you can use it to get started with deep learning in Python, you should probably know a thing or two about neural networks. As you briefly read in the previous section, neural networks found their inspiration and biology, where the term "neural network" can also be used for neurons. The human brain is then an example of such a neural network, which is composed of a number of neurons. And, as you all know, the brain is capable of performing quite complex computations and this is where the inspiration for Artificial Neural Networks comes from. The network a whole is a powerful modeling tool. The most simple neural network is the "perceptron", which, in its simplest form, consists of a single neuron. Much like biological neurons, which have dendrites and axons, the single artificial neuron is a simple tree structure which has input nodes and a single output node, which is connected to each input node. As you can see from the picture, there are six components to artificial neurons. This result will be the input for a transfer or activation function. In the simplest but trivial case, this transfer function would be an identity function, \(f(x) x\) or \(y x\).

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Say goodbye to the glitchy self-checkout scanners in your local retail store. Grocery buying is about to get a big boost from artificial intelligence. At a prototype store in Santa Clara, California, you grab a plastic basket, fill it up as you amble down an aisle packed with all kinds of things--Doritos, hand soap, Coke, and so on--then walk to a tablet computer near the door. The tablet shows a list of everything that's in your basket and how much you owe; you pay, and you leave. This store is actually the demonstration space of a startup called Standard Cognition, which is using a network of cameras and machine vision and deep-learning techniques to create an autonomous checkout experience. Standard Cognition cofounder and chief operating officer Michael Suswal says the company hopes to have it in a store--either a partner's or the company's own--in six months, most likely in the Bay Area.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)