Machine Learning & Data Science

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

data science

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Data Science

Overfitting to your training data can be avoided by evaluating your machine learning algorithm on a holdout test dataset, but what about overfitting to the test data?  Turns out it can be done, easily, and you have to be very careful to avoid it.  But an algorithm from the field of privacy research shows promise for keeping your test data safe from accidental overfitting

Thresholdout: Down with Overfitting

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 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 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

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

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

Competing in a machine learning competition on Kaggle is a kind of rite of passage for data scientists.  Losing unexpectedly at the very end of the contest is also something that a lot of us have experienced.  It's not just bad luck: a very specific combination of overfitting on popular competitions can take someone who is in the top few spots in the final days of a contest and bump them down hundreds of slots in the final tally.

How to Lose at Kaggle

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

This week: everybody's favorite WWII-era classifier metric!  But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

Rock the ROC Curve

We're back again with friend of the pod Walt, former software engineer extraordinaire and current data scientist extraordinaire, to talk about some best practices from software engineering that are ready to jump the fence over to data science.  If last week's episode was for software engineers who are interested in becoming more like data scientists, then this week's episode is for data scientists who are looking to improve their game with best practices from software engineering.

What Data Scientists Can Learn from Software Engineers

This week's episode is just in time for JupyterCon in NYC, August 22-25...

Jupyter notebooks are probably familiar to a lot of data nerds out there as a great open-source tool for exploring data, doing quick visualizations, and packaging code snippets with explanations for sharing your work with others.  If you're not a data person, or you are but you haven't tried out Jupyter notebooks yet, here's your nudge to go give them a try.  In this episode we'll go back to the old days, before notebooks, and talk about all the ways that data scientists like to work that wasn't particularly well-suited to the command line + text editor setup, and talk about how notebooks have evolved over their lifetime to become even more powerful and well-suited to the data scientist's workflow.

Jupyter Notebooks

It's been a busy hurricane season in the Southeastern United States, with millions of people making life-or-death decisions based on the forecasts around where the hurricanes will hit and with what intensity.  In this episode we'll deconstruct those models, talking about the different types of models, the theory behind them, and how they've evolved through the years.

Hurricane Forecasting

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level.  Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices.  We'll go through five key characteristics of great data science teams, which we collectively refer to as "disciplined data science," and why they matter.

Disciplined Data Science

This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows.  If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that crop up in the code when you're trying to go fast.  You take shortcuts, hard-code variable values, skimp on the documentation, and generally write not-that-great code in order to get something done quickly, and then end up paying for it later on.  This is technical debt, and it's particularly easy to accrue with machine learning workflows.  That's the premise of this episode's paper.

Machine Learning: The High Interest Credit Card of Technical Debt

The Fourier transform is one of the handiest tools in signal processing for dealing with periodic time series data. Using a Fourier transform, you can break apart a complex periodic function into a bunch of sine and cosine waves, and figure out what the amplitude, frequency and offset of those component waves are. It's a really handy way of re-expressing periodic data--you'll never look at a time series graph the same way again.

The Fourier Transform

In this episode of the AI in Industry podcast, we speak with Marshall Choy, VP of Product at SambaNova, an AI hardware firm based in the Bay Area. SambaNova was founded by a number of Oracle and Sun Micro Systems alumni. We speak with Choy on two fundamental questions:  How will business models fundamentally change with respect to new AI hardware capabilities? How can business leaders think about their AI hardware needs?  SambaNova is one of many firms that's going to be advertising at the Kisaco Research AI Hardware Summit in Beijing June 4th and 5th.

How Business Leaders Should Think About AI Hardware

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben