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The Practical Quant wrote a new blog post titled The state of machine learning in Apache Spark
[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 of UC Berkeley’s AMPLab, which originated Apache Spark, Apache Mesos, and Alluxio.We began our...
8 days ago
The Practical Quant wrote a new blog post titled Effective mechanisms for searching the space of machine learning algorithms
[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 algorithms. In a recent survey article, Stanley went through the history of neuroevolution and listed...
22 days ago
The Practical Quant wrote a new blog post titled The current state of applied data science
[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 data science can be transformative (including improvements to decision-making or business operations,...
29 days ago
The Practical Quant wrote a new blog post titled The current state of applied data science
[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 data science can be transformative (including improvements to decision-making or business operations,...
29 days ago
The Practical Quant wrote a new blog post titled How Ray makes continuous learning accessible and easy to scale
[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 of RISE Lab. I wanted to get an update on Ray, an open source distributed execution framework that...
36 days ago
The Practical Quant wrote a new blog post titled Why continuous learning is key to AI
[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 data—particularly training data—is essential and remains a key bottleneck among companies wanting to use machine...
46 days ago
The Practical Quant wrote a new blog post titled Why AI and machine learning researchers are beginning to embrace PyTorch
[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, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial Networks), a widely...
50 days ago
The Practical Quant wrote a new blog post titled How big data and AI will reshape the automotive industry
[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 The Big Data Opportunity in Our Driverless Future, and I wanted get his thoughts on the...
64 days ago
The Practical Quant wrote a new blog post titled A framework for building and evaluating data products
[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 social graph, enthusiastic users, and multimedia data, I’ve long regarded Pinterest as a fascinating...
78 days ago
The Practical Quant wrote a new blog post titled Building a next-generation platform for deep learning
[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 scaling current deep learning models requires innovations in both software and hardware. Through his...
83 days ago
The Practical Quant wrote a new blog post titled A scalable time-series database that supports SQL
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Michael Freedman on TimescaleDB and scaling SQL for time-series.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 Michael Freedman, CTO of Timescale and professor of computer science at Princeton University. When I first heard that Freedman and his collaborators were building a time-series database, my immediate reaction was: “Don’t...
92 days ago
The Practical Quant wrote a new blog post titled Programming collective intelligence for financial trading
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.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 Geoffrey Bradway, VP of engineering at Numerai, a new hedge fund that relies on contributions of external data scientists. The company hosts regular competitions where data scientists submit machine...
99 days ago
The Practical Quant wrote a new blog post titled Creating large training data sets quickly
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.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 Alex Ratner, a graduate student at Stanford and a member of Christopher Ré’s Hazy research group. Training data has always been important in building machine learning algorithms, and the rise of data-hungry deep...
106 days ago
The Practical Quant wrote a new blog post titled What are machine learning engineers?
[A version of this appears on the O'Reilly Radar.]A new role focused on creating data products and making data science work in production.by Ben Lorica and Mike LoukidesWe've been talking about data science and data scientists for a decade now. While there’s always been some debate over what “data scientist” means, we've reached the point where many universities, online academies, and bootcamps offer data science programs: master’s degrees, certifications, you name it. The world was a simpler place when we only had statistics. But simplicity isn’t always healthy, and the diversity of data...
108 days ago
The Practical Quant wrote a new blog post titled Data science and deep learning in retail
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.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 Jeremy Stanley, VP of data science at Instacart, a popular grocery delivery service that is expanding rapidly. As Stanley describes it, Instacart operates a four-sided marketplace...
120 days ago
The Practical Quant wrote a new blog post titled Language understanding remains one of AI’s grand challenges
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.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 David Ferrucci, founder of Elemental Cognition and senior technologist at Bridgewater Associates. Ferrucci served as principal investigator of IBM’s DeepQA project and led the Watson team that became champion of...
134 days ago
The Practical Quant wrote a new blog post titled Data preparation in the age of deep learning
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.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 Lukas Biewald, co-founder and chief data scientist at CrowdFlower. In a previous episode we covered how the rise of deep learning is fueling the need for large labeled data sets and high-performance...
141 days ago
The Practical Quant wrote a new blog post titled Scaling machine learning
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.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 Reza Zadeh, adjunct professor at Stanford University, co-organizer of ScaledML, and co-founder of Matroid, a startup focused on commercial applications of deep learning and computer...
155 days ago
The Practical Quant wrote a new blog post titled Architecting and building end-to-end streaming applications
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time 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 Karthik Ramasamy, adjunct faculty member at UC Berkeley, former engineering manager at Twitter, and co-founder of Streamlio. Ramasamy managed the team that built Heron, an open source, distributed stream...
169 days ago
The Practical Quant wrote a new blog post titled Becoming a machine learning engineer
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.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 Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled Hands-on Machine Learning with Scikit-Learn and TensorFlow. Géron’s book is aimed at software...
176 days ago