Data Science at Home

Come join me in our Discord channel speaking about all things data science. Follow me on Twitch during my live coding sessions usually in Rust and Python Our Sponsors The Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com Amethix use advanced Artificial Intelligence and Machine Learning...

Come join me in our Discord channel speaking about all things data science. Follow me on Twitch during my live coding sessions usually in Rust and Python This episode is supported by Monday.com The Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com . This is a public episode. If you wou...

Let's talk about federated learning. Why is it important? Why large organizations are not ready yet?   Come join me in our Discord channel speaking about all things data science. Follow me on Twitch during my live coding sessions usually in Rust and Python This episode is supported by Monday.com The Monday Apps Challenge is bringing developers around the world together to compete in order to build...

Come join me in our Discord channel speaking about all things data science. Follow me on Twitch during my live coding sessions usually in Rust and Python   This episode is supported by Monday.com Monday.com bring teams together so you can plan, manage and track everything your team is working on in one centralized place The monday Apps Challenge is bringing developers around the world together to...

Come join me in our Discord channel speaking about all things data science. Follow me on Twitch during my live coding sessions usually in Rust and Python This episode is supported by Women in Tech by Manning Conferences This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceathome.substack.com

Hey there! Having the best time of my life ;) This is the first episode I record while I am live on my new Twitch channel :) So much fun! Feel free to follow me for the next live streaming. You can also see me coding machine learning stuff in Rust :)) Don't forget to jump on the usual Discord and have a chat I'll see you there!         This is a public episode. If you would like to discuss this wi...

In this episode I speak with Adam Leon Smith , CTO at DragonFly and expert in testing strategies for software and machine learning. We cover testing with deep learning (neuron coverage, threshold coverage, sign change coverage, layer coverage, etc.), combinatorial testing and their practical aspects. On September 15th there will be a live@Manning Rust conference. In one Rust-full day you will atte...

In this episode I speak with Adam Leon Smith , CTO at DragonFly and expert in testing strategies for software and machine learning.   On September 15th there will be a live@Manning Rust conference. In one Rust-full day you will attend many talks about what's special about rust, building high performance web services or video game, about web assembly and much more. If you want to meet the tribe, tu...

After deep learning, a new entry is about ready to go on stage. The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype. This time it's all about privacy and data confidentiality. The new words, homomorphic encryption.   Join and chat with us on the official Discord channel.   Sponsors This episode is supported by Amethix Technologies . Amethix works t...

In this episode I speak about a testing methodology for machine learning models that are supposed to be integrated in production environments. Don't forget to come chat with us in our Discord channel   Enjoy the show!   -- This episode is supported by Amethix Technologies .   Amethix works to create and maximize the impact of the world’s leading corporations, startups, and nonprofits, so they can...

The hype around GPT-3 is alarming and gives and provides us with the awful picture of people misunderstanding artificial intelligence. In response to some comments that claim GPT-3 will take developers' jobs, in this episode I express some personal opinions about the state of AI in generating source code (and in particular GPT-3).   If you have comments about this episode or just want to chat, com...

There is definitely room for improvement in the family of algorithms of stochastic gradient descent. In this episode I explain a relatively simple method that has shown to improve on the Adam optimizer. But, watch out! This approach does not generalize well. Join our Discord channel and chat with us.   References More descent, less gradient Taylor Series   This is a public episode. If you would li...

In this episode I speak about data transformation frameworks available for the data scientist who writes Python code. The usual suspect is clearly Pandas, as the most widely used library and de-facto standard. However when data volumes increase and distributed algorithms are in place (according to a map-reduce paradigm of computation), Pandas no longer performs as expected. Other frameworks play a...

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 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 amazin...

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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceathome.substack.c...

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!     This is a public episode. If you would like to discuss this with other subscribers or get access to bonus epis...

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 su...

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 me...