With the emergence of new powerful hardware accelerators, such as Graphical Processing Units (GPUs) and Programmable Field Gate Arrays (FPGAs), more and more artificial intelligence (AI) has appeared …

Josh Patterson, Vartika Singh, David Kale, and Tom Hanlon walk you through interactively developing and training deep neural networks to analyze digital health data using the Cloudera Workbench and Deeplearning4j (DL4J). You'll learn how to use the Workbench to rapidly explore real-world clinical data, build data-preparation pipelines, and launch training of neural networks.

Deep learning is used broadly at the forefront of research, achieving state-of-the-art results across a variety of domains. However, that doesn't mean it's a fit for all tasksespecially when the constraints of production are considered. Stephen Merity investigates what tasks deep learning excels at, what tasks trigger a failure mode, and where current research is looking to remedy the situation.

Kenneth Stanley offers an overview of the field of neuroevolution, an emerging paradigm for training neural networks through evolutionary principles that has grown up alongside more conventional deep learning, highlighting major algorithms such as NEAT, HyperNEAT, and novelty search, the field's emerging synergies with deep learning, and promising application areas.

Bruno Gonalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of "anything"-2vec methods that use variations of the word2vec neural network architecture.

Jonathan Mugan surveys the field of natural language processing (NLP), both from a symbolic and a subsymbolic perspective, arguing that the current limitations of NLP stem from computers having a lack of grounded understanding of our world. Jonathan then outlines ways that computers can achieve that understanding.

Eric Greene compares different approaches to creating models that predict payment amounts, time, and recipient for recurring expenses such as rent, loans, utilities, and services, outlining the data requirements, feature modeling, and neural network architectures that work best, as well as common issues in training and deploying deep learning networks.

Ben Medlock explores the future of AI, explaining why the potential it holds is not at all frightening. Ben argues that the key to achieving elusive human-like AI lies in a central piece of the puzzle: embodiment.

As interactive and autonomous systems make their way into nearly every aspect of our lives, it is crucial to gain more trust in intelligent systems. Mark Hammond explores the latest techniques and research in building explainable AI systems. Join in to learn approaches for building explainability into control and optimization tasks, including robotics, manufacturing, and logistics.

With TensorFlow, deep machine learning has transitioned from an area of research into mainstream software engineering. Martin Grner walks you through building and training a neural network that recognizes handwritten digits with >99% accuracy using Python and TensorFlow.

Nikolay Manchev offers an overview of the restricted Boltzmann machine, a type of neural network with a wide range of applications, and shares his experience using it on Hadoop (MapReduce and Spark) to process unstructured and semistructured data at a scale.

Kamran Yousaf explains how to substantially accelerate and radically simplify common practices in machine learning, such as running a trained model in production, to meet real-time expectations, using Redis modules that natively store and execute common models generated by Spark ML and TensorFlow algorithms.

Reliable prediction is the ability of a predictive model to explicitly measure the uncertainty involved in a prediction without feedback. Robin Senge shares two approaches to measure different types of uncertainty involved in a prediction.

Rob Craft shares some of the ways machine learning is being used inside of Google, explores cloud-based neural networks, and discusses some customer use cases.

Ted Dunning offers an overview of tensor computingcovering, in practical terms, the high-level principles behind tensor computing systemsand explains how it can be put to good use in a variety of settings beyond training deep neural networks (the most common use case).