Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this mega Ebook is written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step. Click to jump straight to the packages. Jason's book is the best that exists to turn reasonably intelligent individuals with basic programming skills (any language) into sharp machine learning developers. I'm a developer and I feel like I don't really understand something until I can implement it from scratch. I need to understand each piece of it in order to understand the whole. The same thing applies to machine learning algorithms.

Reskilling is the buzzword in the IT sector. With the sector seeing huge churn due to automation and protectionism in the western markets, industry lobby group Nasscom's president R Chandrashekhar told employees in May: Re-skill or perish. The sector is seeing layoffs and voluntary severances. Companies' hiring is on the decline. One estimate even puts the likely job loss at a whopping 2 lakh over the next three years. And in that, the sector is class agnostic.

Introduction to Machine Learning with Python and TensorFlow - This workshop will cover the basics of data science, from cleaning and preparing raw data with pandas, to visualizing different representations of data and relationships between attributes. We will cover the API behind scikit-learn and cover how to set up and run data transformation pipelines, then fit some machine learning models to the data. We will show you how to tune the model to get more accurate results and run cross-validation. In the second part of the session, we will explore how to build more complex models with TensorFlow, a multi-purpose library for linear algebra and tensor manipulation. Using the same APIs provided by scikit-learn, we'll introduce a simple neural network into our data processing pipeline and walk through training a neural net using tabular and image data.

In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that everyone seems to be talking about. The second part is a tensorflow tutorial on getting started, installing and building a small use case. This series is excerpts from a Webinar tutorial series I have conducted as part of the United Network of Professionals. Time to time I will be referring to some of the slides that I used there as part of the talk to make it clearer.

This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Scala: Scala is a general purpose programming language - like Java or C . It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark.

This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Unified Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: •Optimizing cluster setup •Configuring the cluster •Ingesting data •Monitoring long-running jobs Speaker: Tim Hunter, Software Engineer -- Databricks Inc. Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. In today's blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team. I'll also (optionally) demonstrate how you can integrate OpenCV into this setup for a full-fledged computer vision deep learning development environment. To learn more, just keep reading. The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using.

We invite all researchers to submit their manuscripts for review. Please address questions to: hrlnips2017@gmail.com Reinforcement Learning (RL) has become a powerful tool for tackling complex sequential decision-making problems as demonstrated in high-dimensional robotics or game-playing domains. Nevertheless, modern RL methods have considerable difficulties when facing sparse rewards, long planning horizons, and more generally a scarcity of useful supervision signals. Hierarchical Reinforcement Learning (HRL) is emerging as a key component for finding spatio-temporal abstractions and behavioral patterns that can guide the discovery of useful large-scale control architectures, both for deep-network representations and for analytic and optimal-control methods.