Data Science at Home

Rust and machine learning #4: practical tools (Ep. 110)

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 learning (tensorflow) and reinforcement learning (openAI).
Rust is the language of the future.Happy coding! 
Reference
BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
Rust dataframe https://github.com/nevi-me/rust-dataframe
Rustlearn https://github.com/maciejkula/rustlearn
Rusty machine https://github.com/AtheMathmo/rusty-machine
Tensorflow bindings https://lib.rs/crates/tensorflow
Juice (machine learning for hackers) https://lib.rs/crates/juice
Rust reinforcement learning https://lib.rs/crates/rsrl

Rust and machine learning #3 with Alec Mocatta (Ep. 109)

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, regardless of data scale, locally or distributed across a cluster.
Only for June 24th, LDN *Virtual* Talks June 2020 with Bippit (Alec speaking about Amadeus)
 

Rust and machine learning #2 with Luca Palmieri (Ep. 108)

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 data science community (and it's written in Rust, with Python bindings! How cool??!).
 
References
Series Announcement - Zero to Production in Rust https://www.lpalmieri.com/posts/2020-05-10-announcement-zero-to-production-in-rust/
Zero To Production #0: Foreword https://www.lpalmieri.com/posts/2020-05-24-zero-to-production-0-foreword/
Taking ML to production with Rust: a 25x speedup https://www.lpalmieri.com/posts/2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup/

Rust and machine learning #1 (Ep. 107)

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 providing you with an exhaustive list of the benefits of Rust, nor its capabilities. For this you can check the references and start getting familiar with what I think it's going to be the language of the next 20 years.
 
Sponsored
This episode is supported by Pryml Technologies. Pryml offers secure and cost effective data privacy solutions for your organisation. It generates a synthetic alternative without disclosing you confidential data.
 
References
The Rust Programming Language 
Cookin' with Rust
 

Protecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106)

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 applications built on that data back to a production environment.Test ideas. Launch new products. Fast. Secure.

Compressing deep learning models: rewinding (Ep.105)

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-scale platform to synthesise data and deploy applications built on that data back to a production environment.
 
References
Comparing Rewinding and Fine-tuning in Neural Network Pruninghttps://arxiv.org/abs/2003.02389
 

Compressing deep learning models: distillation (Ep.104)

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
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks https://arxiv.org/abs/2004.05937

Pandemics and the risks of collecting data (Ep. 103)

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

Why average can get your predictions very wrong (ep. 102)

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!
 
 

Activate deep learning neurons faster with Dynamic RELU (ep. 101)

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 supported by pryml.io. At pryml we let companies share confidential data. Visit our website.
Don't forget to join us on discord channel to propose new episode or discuss the previous ones. 
References
Dynamic ReLU https://arxiv.org/abs/2003.10027