Data Science at Home

Technology, machine learning and algorithms. Come join the discussion on Discord! https://discord.gg/4UNKGf3

Episodes Date

In this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype an...
July 3, 2020
In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort c...
June 29, 2020
In the 3rd episode of Rust and machine learning I speak with Alec Mocatta. Alec is a +20 year experience professional programmer who has been spending time at the interception of distributed systems a...
June 22, 2020
In the second episode of Rust and Machine learning I am speaking with Luca Palmieri, who has been spending a large part of his career at the interception of machine learning and data engineering. In a...
June 19, 2020
This is the first episode of a series about the Rust programming language and the role it can play in the machine learning field. Rust is one of the most beautiful languages I have ever studied so far...
June 17, 2020
In this episode I have a chat with Sandeep Pandya, CEO at Everguard.ai a company that uses sensor fusion, computer vision and more to provide safer working environments to workers in heavy industry.Sa...
June 15, 2020
As a continuation of the previous episode in this one I cover the topic about compressing deep learning models and explain another simple yet fantastic approach that can lead to much smaller models th...
June 1, 2020
Using large deep learning models on limited hardware or edge devices is definitely prohibitive. There are methods to compress large models by orders of magnitude and maintain similar accuracy during i...
May 20, 2020
Codiv-19 is an emergency. True. Let's just not prepare for another emergency about privacy violation when this one is over.   Join our new Slack channel   This episode is supported by Proton. You can ...
May 8, 2020
Whenever people reason about probability of events, they have the tendency to consider average values between two extremes. In this episode I explain why such a way of approximating is wrong and dange...
April 19, 2020
In this episode I briefly explain the concept behind activation functions in deep learning. One of the most widely used activation function is the rectified linear unit (ReLU). While there are several...
April 1, 2020
One of the best features of neural networks and machine learning models is to memorize patterns from training data and apply those to unseen observations. That's where the magic is. However, there are...
March 23, 2020
In this episode I explain a very effective technique that allows one to infer the membership of any record at hand to the (private) training dataset used to train the target model. The effectiveness o...
March 14, 2020
Masking, obfuscating, stripping, shuffling. All the above techniques try to do one simple thing: keeping the data private while sharing it with third parties. Unfortunately, they are not the silver bu...
March 8, 2020
There are very good reasons why a financial institution should never share their data. Actually, they should never even move their data. Ever.In this episode I explain you why.    
March 1, 2020

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