it is playlist about data science. I'm putting here all podcast in english in related topic. If you know something that should be here please drop me a message with link. And follow me on twitter: @hyzhak_en

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The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

O'Reilly's Ben Lorica chats with Gary Kazantsev, who runs the R&D Machine Learning group at Bloomberg LP, about how big data and data science are making a difference in finance.

Data Science Makes an Impact on Wall Street

In this episode of the O’Reilly Data Show, I spoke with John Akred, cofounder and CTO of Silicon Valley Data Science. Akred and his colleagues teach two of the more popular Strata + Hadoop World tutorials—“Developing a Modern Enterprise Data Strategy” and “Architecting a Data Platform.” We talked about his career in data science and consulting, and his penchant for bringing emerging technologies and tools into large enterprises.

Using Agile Development Techniques for Data Science Projects

In this session of the Super Data Science Podcast, I chat with machine learning expert Hadelin de Ponteves about his work at Google, Canal+ and what career implications may come from the rapid growth of the Data Science field.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/2

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 002 : Machine Learning, Recommender Systems and The Future of Data with Hadelin de Ponteves

What does it take to build a data-driven culture? In this O’Reilly Podcast episode, I pose that question to Ashish Thusoo, founder of Qubole. With his co-founder Joydeep Sen Sarma, Thusoo built a self-service data infrastructure at Facebook beginning in 2007. That infrastructure transformed Facebook’s culture and became part of practically every product decision that the company made.

Building a Data-Driven Culture

In this session of the Super Data Science Podcast, I chat with Ubisoft Finance Director Ulf Morys. You will learn what data science is in computer games, how to build a data landscape and know what are the different ways of using data to save millions.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/8

SDS 008 : Data Science in Computer Games, Learning to Learn and a 40M Euro Case Study with Ulf Morys

In this episode I spoke with Michael Franklin, co-director of UC Berkeley’s AMPLab and chair of the Department of Computer Science at the University of Chicago. AMPLab is well-known in the data community for having originated Apache Spark, Alluxio (formerly Tachyon) and many other open source tools. Today marks the start of a two-day symposium commemorating the end of AMPLab, and we took the opportunity to reflect on its impressive accomplishments.

Building the Next-Generation Big Data Analytics Stack

In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I talk with Richard Socher, chief scientist at Salesforce.  He was previously the founder and CEO of MetaMind, a deep learning startup that Salesforce acquired in 2016.  Socher also teaches the “Deep Learning for Natural Language Processing” course at Stanford University. Our conversation focuses on where deep learning and NLP are headed, and interesting current and near-future applications.

Richard Socher on the Future of Deep Learning

In this episode of the Super Data Science Podcast, I chat with Chief Data Scientist Damian R Mingle. You will learn about how to build models with 95% accuracy rates, know the 3 top tips on quick wins and winning people over; and get ideas on how to save lives with data science.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/13

SDS 013 : 95% Accuracy Models, Winning People Over, and Saving Lives with Damian Mingle

In this episode, I sit down with Brad Knox, founder and CEO of Emoters, a startup building a product called bots_alive—animal-like robots that have a strong illusion of life. We chat about the approach the company is taking, why robots or agents that pass themselves off as human without any transparency should be illegal, and some challenges and applications of reinforcement learning and interactive machine learning.

Brad Knox on Creating a Strong Illusion of Life

In this episode I spoke with Ion Stoica, cofounder and chairman of Databricks. Stoica is also a professor of computer science at UC Berkeley, where he serves as director of the new RISE Lab (the successor to AMPLab). Fresh off the incredible success of AMPLab, RISE seeks to build tools and platforms that enable sophisticated real-time applications on live data, while maintaining strong security. As Stoica points out, users will increasingly expect security guarantees on systems that rely on online machine learning algorithms that make use of personal or proprietary data.

Data is Only as Valuable as the Decisions it Enables

As data scientists add deep learning to their arsenals, they need tools that integrate with existing platforms and frameworks. This is particularly important for those who work in large enterprises. In this episode of the Data Show, I spoke with Adam Gibson, co-founder and CTO of Skymind, and co-creator of Deeplearning4J (DL4J). Gibson has spent the last few years developing the DL4J library and community, while simultaneously building deep learning solutions and products for large enterprises.

The Key to Building Deep Learning Solutions for Large Enterprises

Periscope Data: Long read ad

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 engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

In this episode of the O’Reilly Bots Podcast, I talk about deep learning at the extremes of scale and computing power with Prabhat, who leads the data and analytics group at Lawrence Berkeley National Laboratory’s supercomputing center. If you’re working on commercial AI, it’s worth glancing across the divide at scientific AI.

Prabhat on Deep Learning for Science

DATA SCIENCE FAQs

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 computing systems. CrowdFlower has a service that many leading companies have come to rely on to provide them with labeled data sets to train machine learning models. As deep learning models get larger and more complex, they require training data sets that are bigger than those required by other machine learning techniques.

Data Preparation in the Age of Deep Learning

Turnabout is fair play: That's true in politics, and it's true at Andreessen Horowitz given our internal (and very opinionated!) culture of debate -- where we often agree to disagree, or more often, disagree to agree. So in this special "turnabout" episode of the a16z Podcast, co-founder Marc Andreessen (who is most often in the hot seat being interviewed), got the chance to instead grill fellow partners Frank Chen (who covers AI and much more), Vijay Pande (who covers healthcare for the bio fund), and Alex Rampell (who covers all things fintech).

None of the partners had any idea what Marc would ask them. Putting them in the hot seat at our recent a16z Tech Policy Summit, in Washington, D.C., Marc asked them policy questions such as the implications for tech of the American Health Care Act or AHCA (which itself was being hotly debated that exact same day, just a few miles away); the role of regulatory arbitrage; and what happens to companies big and small if Dodd-Frank is repealed.

Oh, but they also covered so much more: the pros and cons of using tech to "discriminate" for better risk pooling; the role of genetics in addiction (can/should it be used to determine risk?); the opioid crisis (can tech help?); applying AI as a "salve" for everything (what's hyped, what's real, what's easy, what's hard?); the line between redlining and predatory lending (and where/when did sentiment flip?); and the ethics of artificial intelligence (beyond the ole Trolley Problem). Throw in a classic nature vs. nurture debate, a bit of 2-D vs. 3-D, and some fries (yes)... and the future arrives in this episode in 35 minutes or less.

a16z Podcast: What Technology Wants, Needs, Does

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 comprised of retail stores, products within the stores, shoppers assigned to the stores, and customers who order from Instacart. The objective is to get fresh groceries from popular retailers delivered to customers in a timely fashion. Instacart’s goals land them in the center of the many opportunities and challenges involved in building high-impact data products.

Data Science and Deep Learning in Retail

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 learning models for classification tasks. The most promising submissions are then added to an ensemble of models that the company uses to trade in real-world financial markets.

Programming Collective Intelligence for Financial Trading

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Data Science and Deep Learning in Retail

O'Reilly Radar