Data Science at Home

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 can change this very quickly. To make a comparison with the Python ecosystem I will cover frameworks for linear algebra (numpy), dataframes (pandas), off-the-shelf machine learning (scikit-learn), deep...

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 and data analytics. He's the founder of two startups in the distributed system space and author of Amadeus , an open-source framework that encourages you to write clean and reusable code that works, re...

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 addition, Luca contributed to several projects closer to the machine learning community using the Rust programming language. Linfa is an ambitious project that definitely deserves the attention of the...

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. I personally come from the C programming language, though for professional activities in machine learning I had to switch to the loved and hated Python language. This episode is clearly not providin...

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. Sandeep is a senior executive who can hide the complexity of the topic with great talent.   This episode is supported by Pryml.io Pryml is an enterprise-scale platform to synthesise data and deploy app...

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 that still perform as good as the original one. Don't forget to join our Slack channel and discuss previous episodes or propose new ones. This episode is supported by Pryml.io Pryml is an enterprise-sca...

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 inference. In this episode I explain one of the first methods: knowledge distillation  Come join us on Slack Reference Distilling the Knowledge in a Neural Network https://arxiv.org/abs/1503.02531 Know...

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 check them out at protonmail.com or protonvpn.com

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 dangerous, with a numerical example. We are moving our community to Slack . See you there!    

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 flavors of ReLU in the literature, in this episode I speak about a very interesting approach that keeps computational complexity low while improving performance quite consistently. This episode is s...

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 scenarios in which the same machine learning models learn patterns so well such that they can disclose some of the data they have been trained on. This phenomenon goes under the name of unintended m...

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 of such technique is due to the fact that it works on black-box models of which there is no access to the data used for training, nor model parameters and hyperparameters. Such a scenario is very reali...

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 bullet to confidentiality.  All the players in the synthetic data space rely on simplistic techniques that are not secure, might not be compliant and risky for production. At pryml we do things differe...

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.    

Building reproducible models is essential for all those scenarios in which the lead developer is collaborating with other team members. Reproducibility in machine learning shall not be an art, rather it should be achieved via a methodical approach.  In this episode I give a few suggestions about how to make your ML models reproducible and keep your workflow as smooth. Enjoy the show! Come visit us...

Data science and data engineering are usually two different departments in organisations. Bridging the gap between the two is essential to success. Many times the brilliant applications created by data scientists don't find a match in production, just because they are not production-ready. In this episode I have a talk with Daan Gerits, co-founder and CTO at Pryml.io  

Why so much silence? Building a company! That's why :)  I am building pryml , a platform that allows data scientists build their applications on data they cannot get access to.  This is the first of a series of episodes in which I will speak about the technology and the challenges we are facing while we build it.  Happy listening and stay tuned!

In the last episode of 2019 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 and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of...

  This is the fourth and last episode of mini series "The dark side of AI". I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the machine      C: Francesco, today we are starting with an infuriating discussion. Are you ready to be angry?    F: yeah sure is this about brexit?  No, I don’t talk about that. In 1986 the New York City’s Rockefeller...

Get in touch with us Join the discussion about data science, machine learning and artificial intelligence on our Discord server   Episode transcript We always hear the word “metadata”, usually in a sentence that goes like this   Your Honor, I swear, we were not collecting users data, just metadata.   Usually the guy saying this sentence is Zuckerberg, but could be anybody from Amazon or Google. “J...

In 2017 a research group at the University of Washington did a study on the Black Lives Matter movement on Twitter. They constructed what they call a “shared audience graph” to analyse the different groups of audiences participating in the debate, and found an alignment of the groups with the political left and political right, as well as clear alignments with groups participating in other debates...

Chamath Palihapitiya, former Vice President of User Growth at Facebook, was giving a talk at Stanford University, when he said this: “I feel tremendous guilt. The short-term, dopamine-driven feedback loops that we have created are destroying how society works ”. He was referring to how social media platforms leverage our neurological build-up in the same way slot machines and cocaine do, to keep u...

Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept already known to the community: self-attention. In this episode I explain what these mechanisms are, how they work and why they are so powerful. Don't forget to subscribe to our Newsletter  or join the discussio...

Generative Adversarial Networks or GANs are very powerful tools to generate data. However, training a GAN is not easy. More specifically, GANs suffer of three major issues such as instability of the training procedure, mode collapse and vanishing gradients.   In this episode I not only explain the most challenging issues one would encounter while designing and training Generative Adversarial Netwo...

What happens to a neural network trained with random data? Are massive neural networks just lookup tables or do they truly learn something?  Today’s episode will be about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski from New York University. Stan spent two summers as a visiting student with Prof. Yoshua Bengio  and has been working on  Understanding and improving ho...