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Our popular course Introduction to QuantLib Development will be taking place June 18-20th, 2018.

 

The Practical Quant's Blog

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The importance of transparency and user control in machine learning

April 12, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on our lives. We live in an age when many of our interactions with companies and services are governed by algorithms. At a time...

What machine learning engineers need to know

March 29, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.In this episode of the Data Show, I spoke Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that...

How to train and deploy deep learning at scale

March 15, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine...

Using machine learning to monitor and optimize chatbots

March 6, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.In this episode of the Data Show, I spoke with Ofer Ronen, GM of Chatbase, a startup housed within Google’s Area 120. With tools for building chatbots becoming accessible, conversational interfaces are becoming more prevalent. As Ronen highlights in our conversation, chatbots are already enabling companies to automate many routine tasks (mainly in customer...

Unleashing the potential of reinforcement learning

March 1, 2018 Comments (0)

[A version of they post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized.In this episode of the Data Show, I spoke with Danny Lange, VP of AI and machine learning at Unity Technologies. Lange previously led data and machine learning teams at Microsoft, Amazon, and Uber, where his teams were responsible for building data science tools used by other developers and analysts...

Graphs as the front end for machine learning

February 15, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations.In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for...

Machine learning needs machine teaching

February 1, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.In this episode of the Data Show, I spoke with Mark Hammond, founder and CEO of Bonsai, a startup at the forefront of developing AI systems in industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited...

Introducing RLlib: A composable and scalable reinforcement learning library

January 19, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]RISE Lab’s Ray platform adds libraries for reinforcement learning and hyperparameter tuning.In a previous post, I outlined emerging applications of reinforcement learning (RL) in industry. I began by listing a few challenges facing anyone wanting to apply RL, including the need for large amounts of data, and the difficulty of reproducing research results and deriving the error estimates needed for mission-critical applications....

How machine learning can be used to write more secure computer programs

January 18, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.In this episode of the Data Show, I spoke with Fabian Yamaguchi, chief scientist at ShiftLeft. His 2015 Ph.D. dissertation sketched out how the combination of static analysis, graph mining, and machine learning, can be used to develop tools to augment security analysts. In a recent post, I argued for machine...

Responsible deployment of machine learning

January 11, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]We need to build machine learning tools to augment our machine learning engineers.In this post, I share slides and notes from a talk I gave in December 2017 at the Strata Data Conference in Singapore offering suggestions to companies that are actively deploying products infused with machine learning capabilities. Over the past few years, the data community has focused on infrastructure and platforms for data collection, including robust...