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.

1. BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms

In the 3rd episode of Rust and machine learning I speak with Alec Mocatta.

Only for June 24th, LDN *Virtual* Talks June 2020 with Bippit (Alec speaking about Amadeus)

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.

• Series Announcement - Zero to Production in Rust https://www.lpalmieri.com/posts/2020-05-10-announcement-zero-to-production-in-rust/

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.

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.

• The Rust Programming Language 

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.

This episode is supported by Pryml.io

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

Comparing Rewinding and Fine-tuning in Neural Network Pruning

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

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• Distilling the Knowledge in a Neural Network https://arxiv.org/abs/1503.02531

Codiv-19 is an emergency. True. Let's just not prepare for another emergency about privacy violation when this one is over.

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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. 

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). 

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. 

Dynamic ReLU https://arxiv.org/abs/2003.10027