data science

In this episode, I tackle three often conflated terms - AI, machine learning, and deep learning - to shine some light on what exactly they are.

Additional materials: www.superdatascience.com/464

The SuperDataScience Podcast: Machine Learning | AI | Success

SDS 464: A.I. vs Machine Learning vs Deep Learning

A powerful portrait of war and retribution. A beautiful story of love and forgiveness.

"Words are power. They can bring you down, lift you up, make your heart soar, make you fall in love. Or make you hate."

Paris 1944. Elise Chevalier knows what it is to love…and to hate. Her fiancé, a young French soldier, was killed by the German army at the Maginot Line. Living amongst the enemy, Elise must keep her rage buried deep within.

Sebastian Kleinhaus no longer recognizes himself. Forced to join the Third Reich and wear a uniform he despises, he longs for a way out. For someone, anyone, to be his salvation.

Brittany 1963. Reaching for the suitcase under her mother’s bed, eighteen-year-old Josephine Chevalier uncovers a secret that shakes her to the core. Determined to find the truth, she travels to Paris where she learns the story of a forbidden love as a city fought for its freedom. Of the last stolen hours before the first light of liberation. And of a betrayal so deep that it would irrevocably change the course of two young lives life forever.

Available July 19, 2022 from Hachette Audio as a digital download, and in Print and Ebook from Grand Central Publishing. 

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The Last Hours In Paris by Ruth Druart Read by Daphne Kouma, Jess Nesling, and Ben Jacobsen - Audio

In 1977, a mild-mannered aeronautical engineer sideswiped a parked car in Compton, CA. He stopped his car to see the damage, and all of a sudden a man opened his car door, and jumped in the car. He said, "I'm about to save your life." What happened next would torture him for the rest of his life.

Ep 11: I'm About to Save Your Life

The Feynman Series (Part 5) - Think Like A Martian

Programa superhipermegaespecial com a participação de TODOS tenistas brasileiros que estão nas chaves principais em Roland Garros e muitos especialistas convidados avaliando as expectativas para o Grand Slam do saibro. 

Quadra 18: Especial Pré-Roland Garros

Ready or Not  at Home

Brian Tyler USGA THEME 1 MINUTE VRSN

Blah bleh blah ???????

In this episode of the SuperDataScience Podcast, I chat with the Deep Learning Expert Hadelin de Ponteves. You will be able to have a look at the 6 supervised and unsupervised deep learning models, learn the idea to the question: why deep learning instead of machine learning, and discover TensorFlow and PyTorch as the two major tools in Deep Learning.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/47

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

SDS 047: An Expert Overview of the Deep Learning Models, Supervised and Unsupervised

In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta. Burt recently co-authored an upcoming white paper on managing risk in machine learning models, and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning.

Some high-profile examples of models gone awry have raised awareness among companies for the need for better risk management tools and processes. There is now a growing interest in ethics among data scientists, specifically in tools for monitoring bias in machine learning models. In a previous post, I listed some of the key considerations organization should keep in mind as they move models to production, but the upcoming report co-authored by Burt goes far beyond and recommends lines of defense, including a description of key roles that are needed.

Managing Risk in Machine Learning Models

In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.

Why It's Hard to Design Fair Machine Learning Models

Deblina Bhattacharjee joins us to talk about her amazing work in computer vision and give advice for getting into and excelling in the field.

In this episode you will learn:
• Deblina’s master’s program work [4:03]
• Deblina’s computer vision research and Ph.D. [11:46]
• Deblina’s drumming hobby [20:18]
• The daily work [24:40]
• What key skills do you need as a data scientist? [33:21]
• How can a data scientist prepare for the future? [37:03]
• How does Deblina tackle time management? [40:24]

Additional materials: www.superdatascience.com/439

SDS 439: Deep Learning for Machine Vision

This week I answer your questions about machine learning and how to educate yourself further in the field.

Additional materials: www.superdatascience.com/446

SDS 446: Getting Started in Machine Learning

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.

Why It's Hard to Design Fair Machine Learning Models

O'Reilly Radar

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