SoftBank's robot, 'Pepper', performs during a news conference in Taipei Thomson Reuters Drew Breunig works with data, technology, advertising and more. He works on business applications at @PlaceIQ and is Co-founder at @GetReporter. Follow him on Twitter at @dbreunig. As buzzwords become ubiquitous they become easier to tune out. We've finely honed this defense mechanism, for good purpose. It's better to focus on what's in front of us than the flavor of the week.

Many of them are new to this set of technologies, and seeing the topic through my students' eyes has made me realize how overwhelming it can be. There are so many different types of AI, each requiring some technical knowledge to fully grasp, that newcomers to the field often have difficulty figuring out how to jump in. In the simplest case, cognitive technologies can be just more autonomous extensions of traditional analytics -- automatically running every possible combination of predictive variables in a regression analysis, for example. More complex types of cognitive technology -- neural or deep learning networks, natural language processing, and algorithms -- can seem like black boxes even to the data scientists who create them. Though these technologies can seem daunting, the good news is that getting started with cognitive technologies is getting easier all the time.

Self-driving cars are getting closer and closer to become an everyday reality. Although, at first it may seem like that autonomous cars investigations are reserved for a very narrow group of researchers, we would like to show it is not necessary true. Actually, the only things you need to start playing with driverless-cars, are some hacking skills, a little bit of programming and basic understanding of machine learning concepts - mainly deep and reinforcement learning. Driverless cars have been a dream of engineers since automotive industry was born and the first approaches were made, when Ford Model T was still ruling the roads. Although, radio-controlled car, presented by Houdina Radio Control in 1925, is far away from what we understand as an autonomous car in 21th century, it might be considered as the first try to construct an automobile, that does not require a human behind the wheel.

We still have a great deal on the Deep Learning and Artificial Intelligence Introductory Bundle in the Geeky Gadgets Deals store, you can save 91% off the regular price. The Deep Learning and Artificial Intelligence Introductory Bundle normally costs $480 and we have it available in our deals store for just $39. Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic." One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the "line of best fit." This is the first step to building machines that, in effect, act like neurons in a neural network as they learn while they're fed more information.

In much of machine learning, data used for training and inference undergoes a preprocessing step, where multiple inputs (such as images) are scaled to the same dimensions and stacked into batches. This lets high-performance deep learning libraries like TensorFlow run the same computation graph across all the inputs in the batch in parallel. Batching exploits the SIMD capabilities of modern GPUs and multi-core CPUs to speed up execution. However, there are many problem domains where the size and structure of the input data varies, such as parse trees in natural language understanding, abstract syntax trees in source code, DOM trees for web pages and more. In these cases, the different inputs have different computation graphs that don't naturally batch together, resulting in poor processor, memory, and cache utilization. Today we are releasing TensorFlow Fold to address these challenges.

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This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC.

In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional autoencoders and is trained through a new unsupervised loss function. During training, we optimize both the reconstruction accuracy of input samples and the alignment between a kernel matrix given as prior and the inner products of the hidden representations computed by the autoencoder. Kernel alignment provides control over the hidden representation learned by the autoencoder. Experiments have been performed to evaluate both reconstruction and kernel alignment performance. Additionally, we applied our method to emulate kPCA on a denoising task obtaining promising results.

This paper studies the cooperative training of two probabilistic models of signals such as images. Both models are parametrized by convolutional neural networks (ConvNets). The first network is a descriptor network, which is an exponential family model or an energy-based model, whose feature statistics or energy function are defined by a bottom-up ConvNet, which maps the observed signal to the feature statistics. The second network is a generator network, which is a non-linear version of factor analysis. It is defined by a top-down ConvNet, which maps the latent factors to the observed signal. The maximum likelihood training algorithms of both the descriptor net and the generator net are in the form of alternating back-propagation, and both algorithms involve Langevin sampling. We observe that the two training algorithms can cooperate with each other by jumpstarting each other's Langevin sampling, and they can be naturally and seamlessly interwoven into a CoopNets algorithm that can train both nets simultaneously.

Deep Learning, as a branch of Machine Learning, employs algorithms to process data and imitate the thinking process, or to develop abstractions. Deep Learning (DL) uses layers of algorithms to process data, understand human speech, and visually recognize objects. Information is passed through each layer, with the output of the previous layer providing input for the next layer. The first layer in a network is called the input layer, while the last is called an output layer. All the layers between the two are referred to as hidden layers.