Ensemble methods are arguably the most trustworthy techniques for boosting the performance of machine learning models. Popular independent ensembles (IE) relying on naïve averaging/voting scheme have been of typical choice for most applications involving deep neural networks, but they do not consider advanced collaboration among ensemble models. In this paper, we propose new ensemble methods specialized for deep neural networks, called confident multiple choice learning (CMCL): it is a variant of multiple choice learning (MCL) via addressing its overconfidence issue. In particular, the proposed major components of CMCL beyond the original MCL scheme are (i) new loss, i.e., confident oracle loss, (ii) new architecture, i.e., feature sharing and (iii) new training method, i.e., stochastic labeling. We demonstrate the effect of CMCL via experiments on the image classification on CIFAR and SVHN, and the foregroundbackground segmentation on the iCoseg. In particular, CMCL using 5 residual networks provides 14.05% and 6.60% relative reductions in the top-1 error rates from the corresponding IE scheme for the classification task on CIFAR and SVHN, respectively.

An ability to model a generative process and learn a latent representation for speech in an unsupervised fashion will be crucial to process vast quantities of unlabelled speech data. Recently, deep probabilistic generative models such as Variational Autoencoders (VAEs) have achieved tremendous success in modeling natural images. In this paper, we apply a convolutional VAE to model the generative process of natural speech. We derive latent space arithmetic operations to disentangle learned latent representations. We demonstrate the capability of our model to modify the phonetic content or the speaker identity for speech segments using the derived operations, without the need for parallel supervisory data.

File photo: Tesla Chief Executive Elon Musk smiles as he attends a forum on startups in Hong Kong, China January 26, 2016. Despite Elon Musk's continued warnings, evil machines won't take over the world, two experts said this week. Artificial intelligence (AI) could be destined to turn against humanity, Musk has argued. The tech exec, who in addition to running high-profile companies such as Tesla and SpaceX, is a co-founder of OpenAI, a non-profit AI research company "discovering and enacting the path to safe artificial general intelligence." However, other executives in Silicon Valley have taken issue with Musk's comments, including the leader of Google's artificial intelligence efforts. "I'm definitely not worried about the AI apocalypse," said Google's John Giannandrea, when speaking at TechCrunch Disrupt SF. "I just object to the hype and soundbites that some people are making," he added.

Deep learning will be a key driver of performance in demand, fraud and failure predictions by 2019, according to Gartner. The technology builds upon machine learning with the provision of intermediate data representations. Applications changing focus to predictive analysis from image, text and audio processing need business buy-in, infrastructure and talented data scientists. Approximately 80% of data scientists believe the variation of machine learning will be part of their tool kits by next year. Although deep learning and machine learning offer tremendous data processing capabilities, Gartner cautioned that the technologies are not a replacement for human workers.

Technologies like artificial intelligence and deep learning are driving drone's evolution and fueling their autonomous future, according to Jesse …

Even as machines known as "deep neural networks" have learned to converse, drive cars, beat video games and Go champions, dream, paint pictures and help make scientific discoveries, they have also confounded their human creators, who never expected so-called "deep-learning" algorithms to work so well. No underlying principle has guided the design of these learning systems, other than vague inspiration drawn from the architecture of the brain (and no one really understands how that operates either). Like a brain, a deep neural network has layers of neurons -- artificial ones that are figments of computer memory. When a neuron fires, it sends signals to connected neurons in the layer above. During deep learning, connections in the network are strengthened or weakened as needed to make the system better at sending signals from input data -- the pixels of a photo of a dog, for instance -- up through the layers to neurons associated with the right high-level concepts, such as "dog." After a deep neural network has "learned" from thousands of sample dog photos, it can identify dogs in new photos as accurately as people can.

Here are 29 resources, mostly in the form of tutorials, covering most important topics in data science: This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, sign up on DSC.

Much like the rise of electricity, which started about 100 years ago, AI will revolutionize every major industry. Andrew Ng explains how AI can transform your business, shares major technology trends and thoughts on where your biggest future opportunities may lie, and explores best practices for incorporating AI, machine learning, and deep learning into your organization. Follow O'Reilly on: Twitter: http://twitter.com/oreillymedia

First, data is divided into many nodes (taking the place of neurons). This data gets transmitted to other nodes, which manipulate this data in some ways, so as to make it better usable for the end goal of the system. The most often way the data gets manipulated, is by assigning a weight to it. The weight determines how valuable that information is for the desired output. However, this is where the differences between neurons and Deep Learning come into play.

Since the beginning of the brief history of Natural Language Processing (NLP), there has been the need to transform text into something a machine can understand. That is, transforming text into a meaningful vector (or array) of numbers. The de-facto standard way of doing this in the pre-deep learning era was to use a bag of words approach. The idea behind this method is very simple, though very powerful. First, we define a fixed length vector where each entry corresponds to a word in our pre-defined dictionary of words.