A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go. These games have a discrete action space. However, many real-world reinforcement learning domains have continuous action spaces, for example in robotic control, navigation and self-driving cars. This paper presents the necessary theoretical extensions of Alpha Zero to deal with continuous action space. We also provide some preliminary experiments on the Pendulum swing-up task, empirically showing the feasibility of our approach. Thereby, this work provides a first step towards the application of iterated search and learning in domains with a continuous action space.

We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1% BLEU improvement absolute and it allows to train deeper recurrent encoder networks. Promising preliminary results on max. expected BLEU training are presented. We are able to train state-of-the-art models for translation and end-to-end models for speech recognition and show results on WMT 2017 and Switchboard. The flexibility of RETURNN allows a fast research feedback loop to experiment with alternative architectures, and its generality allows to use it on a wide range of applications.

In this talk, Richard Garris, Principal Architect at Databricks will explain how various ML algorithms are parallelized in Apache Spark. Andrew Ng calls the algorithms the "rocket ship" and the data "the fuel that you feed machine learning" to build deep learning applications. We will start with an understanding of machine learning pipelines built using single machine algorithms including Pandas, scikit-learn, and R. Then we will discuss how Apache Spark MLlib can be used to parallelize your machine learning pipeline with Linear Regression and Random Forest. Lastly, we will discuss ways to parallelize single machine algorithms in Spark by broadcasting the data and then performing distributed feature selection, model creation or hyperparameter tuning. Bio: Richard Garris is a Principal Solutions Architect at Databricks focused on helping clients with their Advanced Analytics initiatives using Apache Spark and MLlib.

It was too much fun creating my own convolutional neural network with Watson Studio! The Neural Network Modeler provides expressive and intuitive graphical tools for building powerful deep learning models. Application developers, analysts, and data scientists use WML to integrate machine learning into their workflow seamlessly. For example, you can save and deploy models as a REST API with a few clicks, minimizing the time it takes to put machine learning models to work. In this example, I used the familiar MNIST data set (images of hand-written digits) and created a three layers deep CNN.

AI Dev Con Intel announced a range of machine learning software tools and hinted at new chips on Wednesday, including its first commercial AI ASIC, the NNP-L1000, launching in 2019. Rao was CEO and co-founder of Nervana, a deep learning startup that was acquired by Intel in 2016. AI hype cycles have led to multiple booms and busts, Rao explained. At the moment, its popularity is rising rapidly and its greedily slurping all the data and compute available. The AI revolution is really a computing revolution.

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This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

With the development of wearable cameras, first-person activity recognition has been a popular topic in recent years [1]. There are many conventional approaches which tackle first-person activity recognition. Some of these approaches employ motion feature such as optical flow and also a classifier, e.g., LogitBoost and SVM (support vector machine)[2, 3]. In recent years, DCNN (deep convolutional neural network), the state-of-the-art model for visual recognition, has been proposed [4] and then applied to several tasks on first-person activity recognition. Although DCNN models provide remarkable results for image recognition, they require a large amount of labeled training samples.

Artificial intelligence (AI) is playing an increasingly critical role in business. By 2020, 30 percent of organizations that fail to apply AI will not be operationally and economically viable, according to one report[1]. And in a survey, 91 percent of infrastructure and operations leaders cite "data" as a main inhibitor of AI initiatives[2]. What does a data professional need to know about AI and its data requirements in order to support his or her organization's AI efforts? Many factors have converged in recent years to make AI viable, including the growth of processing power and advances in AI techniques, notably in the area of deep learning (DL).

The need for new medications is higher than ever, but so is the cost and time to bring them to market. Developing a new drug can cost billions and take as long as 14 years, according to the U.S. Food and Drug Administration. Yet with all that effort, only 8 percent of drugs make it to market, the FDA said. "We need to make smarter decisions about which potential medicines we develop and test," said Abraham Heifets, co-founder of San Francisco-based startup Atomwise. The six-year-old company, a member of our Inception startup incubator program, is working to make that happen by using GPU-accelerated deep learning to predict which molecules are most likely to lead to treatments.