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.

Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That's why we have cheat sheets. But there are a plethora of cheat sheets available out there, choosing the right cheat sheet is a tough task. So, I decided to write this article.

Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software(ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India -Not still cheap), availability of data, good books and MOOCS. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera In this post I talk about 5 aspects of each course which will help you decide. I came across this course when reading an article in kddnudgets . For the first time I heard about Jeremy Howard, searched about him in Wikipedia and was impressed .

Today's leading minds talk AI with host Byron Reese Visit VoicesInAI.com to access the podcast, or subscribe now: Byron Reese: Hello, this is Voices in AI brought to you by Gigaom. I am your host, Byron Reese. Jeff is a Google Senior Fellow and he leads the Google Brain project. His work probably touches my life, and maybe yours, about every hour of every day, so I can't wait to begin the conversation. Welcome to the show Jeff. Jeff Dean: Hi Byron, this is Jeff Dean. I'm really good, Jeff, thanks for taking the time to chat. You went to work for Google, I believe, in the second millennium.

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