Data Science at Home
Francesco Gadaleta
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Top 10 Data Science at Home Episodes
Goodpods has curated a list of the 10 best Data Science at Home episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to Data Science at Home for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite Data Science at Home episode by adding your comments to the episode page.
Harnessing AI for Cybersecurity: Expert Tips from QFunction (Ep. 258)
Data Science at Home
06/11/24 • 34 min
In this episode, we sit down with Ryan Smith, Founder of QFunction LLC, to explore how AI and machine learning are revolutionizing cybersecurity. With over 8 years of experience, including work at NASA's Jet Propulsion Laboratory, Ryan shares insights on the future of threat detection and prevention, the challenges businesses face in maintaining effective cybersecurity, and the ethical considerations of AI implementation.
Learn about cost-effective strategies for small businesses, the importance of collaboration in combating cyber threats, and how QFunction tailors its AI solutions to meet diverse industry needs.
- Arctic Wolf Learn what the new year holds for ransomware as a service, Active Directory, artificial intelligence and more when you download the 2024 Arctic Wolf Labs Predictions Report today at arcticwolf.com/datascience
- Intrepid AI (https://intrepid.ai) is an AI assisted all-in-one platform for robotics teams. Build robotics applications in minutes, not months.
- QFunction does cybersecurity differently. By relying on scientific breakthroughs in AI and machine learning, QFunction works within your existing security stack to detect anomalies and threats within your data
Episode 66: More intelligent machines with self-supervised learning
Data Science at Home
06/25/19 • 18 min
In this episode I talk about a new paradigm of learning, which can be found a bit blurry and not really different from the other methods we know of, such as supervised and unsupervised learning. The method I introduce here is called self-supervised learning.
Enjoy the show!
Don't forget to subscribe to our Newsletter at amethix.com and get the latest updates in AI and machine learning. We do not spam. Promise! ReferencesDeep Clustering for Unsupervised Learning of Visual Features
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
Episode 39: What is L1-norm and L2-norm?
Data Science at Home
07/19/18 • 21 min
In this episode I explain the differences between L1 and L2 regularization that you can find in function minimization in basically any machine learning model.
Episode 33: Decentralized Machine Learning and the proof-of-train
Data Science at Home
06/11/18 • 17 min
In the attempt of democratizing machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model that is privately trained should be verified and uniquely identified across its entire life cycle, from its random initialization to setting the optimal values of its parameters.
How does blockchain allow all this? Fitchain is the decentralized machine learning platform that provides models an identity and a certification of their training procedure, the proof-of-train
Episode 30: Neural networks and genetic evolution: an unfeasible approach
Data Science at Home
11/21/17 • 22 min
Despite what researchers claim about genetic evolution, in this episode we give a realistic view of the field.
What happens to data transfer after Schrems II? (Ep. 131)
Data Science at Home
12/04/20 • 31 min
In this episode Adam Leon Smith, CTO of DragonFly and expert in data regulations explains some of the consequences of Schrems II and data transfers from EU to US.
For very interesting references and a practical example, subscribe to our Newsletter
What's happening with AI today? (Ep. 164)
Data Science at Home
08/11/21 • 25 min
Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.
Links- AI Today podcast: http://aitoday.live/
- CPMAI Methodology: https://www.cognilytica.com/cpmai-methodology/
AI’s Impact on Software Engineering: Killing Old Principles? [RB] (Ep. 229)
Data Science at Home
05/25/23 • 13 min
In this episode, we dive into the ways in which AI and machine learning are disrupting traditional software engineering principles. With the advent of automation and intelligent systems, developers are increasingly relying on algorithms to create efficient and effective code. However, this reliance on AI can come at a cost to the tried-and-true methods of software engineering. Join us as we explore the pros and cons of this paradigm shift and discuss what it means for the future of software development.
Sponsors BloombergAt Bloomberg, they solve complex, real-world problems for customers across the global capital markets. From real-time market data to sophisticated analytics, powerful trading tools, and more, Bloomberg engineers work with systems that operate at scale.
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Arctic Wolf
Cybercriminals are evolving. Their techniques and tactics are more advanced, intricate, and dangerous than ever before. Industries and governments around the world are fighting back, unveiling new regulations meant to better protect data against this rising threat. Arctic Wolf — the leader in security operations — is on a mission to end cyber risk by giving organizations the protection, information, and confidence they need to protect their people, technology, and data.
Visit arcticwolf.com/datascience to take your first step.
Episode 4: BigData on your desk
Data Science at Home
07/23/15 • 16 min
Have you ever thought to get a Big Data infrastructure on your desk? That’s right! On your desk.
Episode 46: why do machine learning models fail? (Part 2)
Data Science at Home
09/04/18 • 17 min
In this episode I continue the conversation from the previous one, about failing machine learning models.
When data scientists have access to the distributions of training and testing datasets it becomes relatively easy to assess if a model will perform equally on both datasets. What happens with private datasets, where no access to the data can be granted?
At fitchain we might have an answer to this fundamental problem.
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FAQ
How many episodes does Data Science at Home have?
Data Science at Home currently has 247 episodes available.
What topics does Data Science at Home cover?
The podcast is about News, Tech News, Podcasts and Technology.
What is the most popular episode on Data Science at Home?
The episode title 'Rust and machine learning #4: practical tools (Ep. 110)' is the most popular.
What is the average episode length on Data Science at Home?
The average episode length on Data Science at Home is 27 minutes.
How often are episodes of Data Science at Home released?
Episodes of Data Science at Home are typically released every 7 days, 16 hours.
When was the first episode of Data Science at Home?
The first episode of Data Science at Home was released on Jul 8, 2015.
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