Remember me

Register  |   Lost password?


Sign up here to let us know if you are interested in joining us for our Introduction to QuantLib Course later in the year.

 

The Practical Quant's Blog

The Practical Quant Blog Header

The state of machine learning in Apache Spark

September 14, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Ion Stoica and Matei Zaharia explore the rich ecosystem of analytic tools around Apache Spark.In this episode of the Data Show, we look back to a recent conversation I had at the Spark Summit in San Francisco with Ion Stoica (UC Berkeley professor and executive chairman of Databricks) and Matei Zaharia (assistant professor at Stanford and chief technologist of Databricks). Stoica and Zaharia were core members...

Effective mechanisms for searching the space of machine learning algorithms

August 31, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Kenneth Stanley on neuroevolution and other principled ways of exploring the world without an objective.In this episode of the Data Show, I spoke with Ken Stanley, founding member of Uber AI Labs and associate professor at the University of Central Florida. Stanley is an AI researcher and a leading pioneer in the field of neuroevolution—a method for evolving and learning neural networks through evolutionary...

The current state of applied data science

August 24, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning.As we enter the latter part of 2017, it’s time to take a look at the common challenges faced by companies interested in using data science and machine learning (ML). Let’s assume your organization is already collecting data at a scale that justifies the use of analytic tools, and that you’ve managed to identify and prioritize use cases where...

The current state of applied data science

August 24, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning.As we enter the latter part of 2017, it’s time to take a look at the common challenges faced by companies interested in using data science and machine learning (ML). Let’s assume your organization is already collecting data at a scale that justifies the use of analytic tools, and that you’ve managed to identify and prioritize use cases where...

How Ray makes continuous learning accessible and easy to scale

August 17, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications.Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.In this episode of the Data Show, I spoke with Robert Nishihara and Philipp Moritz, graduate students at UC Berkeley and members...

Why continuous learning is key to AI

August 7, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]A look ahead at the tools and methods for learning from sparse feedback.As more companies begin to experiment with and deploy machine learning in different settings, it’s good to look ahead at what future systems might look like. Today, the typical sequence is to gather data, learn some underlying structure, and deploy an algorithm that systematically captures what you’ve learned. Gathering, preparing, and enriching the right...

Why AI and machine learning researchers are beginning to embrace PyTorch

August 3, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Soumith Chintala on building a worthy successor to Torch and deep learning within Facebook.Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.In this episode of the Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects,...

How big data and AI will reshape the automotive industry

July 20, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Evangelos Simoudis on next-generation mobility services.Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.In this episode of the Data Show, I spoke with Evangelos Simoudis, co-founder of Synapse Partners and a frequent contributor to O’Reilly. He recently published a book entitled...

A framework for building and evaluating data products

July 6, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Pinterest data scientist Grace Huang on lessons learned in the course of machine learning product launches.Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.In this episode of the Data Show, I spoke with Grace Huang, data science lead at Pinterest. With its combination of a large...

Building a next-generation platform for deep learning

June 30, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Naveen Rao on emerging hardware and software infrastructure for AI.Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.In this episode of the Data Show, I speak with Naveen Rao, VP and GM of the Artificial Intelligence Products Group at Intel. In an earlier episode, we learned that...