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

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

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

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 Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early tools for privacy-preserving analytics, both for improving decision-making (business intelligence and analytics) and for enabling automation (machine learning). One of the tools I mentioned is an open source project for SQL-based analysis that adheres to state-of-the-art differential privacy (a formal guarantee that provides robust privacy assurances).  Since business intelligence typically relies on SQL databases, this open source project is something many companies can already benefit from today.

What about machine learning? While I didn’t have space to point this out in my previous post, differential privacy has been an area of interest to many machine learning researchers. Most practicing data scientists aren’t aware of the research results, and popular data science tools haven’t incorporated differential privacy in meaningful ways (if at all). But things will change over the next months. For example, Liu wants to make  ideas from differential privacy accessible to industrial data scientists, and she is part of a team building tools to make this happen.

How Privacy-Preserving Techniques Can Lead to More Robust 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

Erica Greene joins us to discuss her work as a machine learning manager at Etsy, how they tackle problem-solving, how they implement ML scaling, and more.

In this episode you will learn:
• Erica’s role at Etsy and problem solving between platforms [2:28]
• Interesting failures Erica has navigated [25:40]
• How does Erica’s team select problems to solve [33:07]
• Engineering at scale [40:15]
• What does Erica’s working day look like? [46:30]
• Etsy is hiring [53:00]
• Diversity in hiring [57:12]
• Do data scientists need PhDs? [1:01:26]

Additional materials: www.superdatascience.com/435

SDS 435: Scaling Up Machine Learning

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

data science

Ramona Wimmer