Data Science at Home

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 create a better future for everyone they serve. They are a consulting firm focused on data science, machine learning, and artificial intelligence.

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, come join us on the official Discord channel.

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 create a better future for everyone they serve. They are a consulting firm focused on data science, machine learning, and artificial intelligence.

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.

• More descent, less gradient

In this episode I speak about data transformation frameworks available for the data scientist who writes Python code.

In this episode I explain the frameworks that are the best equivalent to Pandas in bigdata contexts.

Don't forget to join our Discord channel and comment previous episodes or propose new ones.

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 create a better future for everyone they serve. Amethix is a consulting firm focused on data science, machine learning, and artificial intelligence.

Pandas a fast, powerful, flexible and easy to use open source data analysis and manipulation tool - https://pandas.pydata.org/

Modin - Scale your pandas workflows by changing one line of code - https://github.com/modin-project/modin

Dask advanced parallelism for analytics https://dask.org/

Ray is a fast and simple framework for building and running distributed applications https://github.com/ray-project/ray

RAPIDS - GPU data science https://rapids.ai/

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 amazing field of research, not to bring disillusion in the near future.

Join us to our Discord channel to discuss your favorite episode and propose new ones.

This episode is brought to you by Protonmail

Click on the link in the description or go to protonmail.com/datascience and get 20% off their annual subscription.

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