Archive for June 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 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

  1. BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
  2. Rust dataframe https://github.com/nevi-me/rust-dataframe
  3. Rustlearn https://github.com/maciejkula/rustlearn
  4. Rusty machine https://github.com/AtheMathmo/rusty-machine
  5. Tensorflow bindings https://lib.rs/crates/tensorflow
  6. Juice (machine learning for hackers) https://lib.rs/crates/juice
  7. Rust reinforcement learning https://lib.rs/crates/rsrl

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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)

 

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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

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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

 

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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.

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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 Pruning
https://arxiv.org/abs/2003.02389

 

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