Data Science at Home

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**Data Science at Home**

Cutting through AI bullsh*t.
Come join the discussion on Discord!
https://discord.gg/4UNKGf3

Latest Episodes:

Reinforcement Learning is all you need. Or is it? (Ep. 165) Aug 17, 2021

Show notes

Is reinforcement learning sufficient to build truly intelligent machines? Listen to this episode to find out.

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Quantum Metric

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Amethix Technologies

Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

References

What's happening with AI today? (Ep. 164) Aug 11, 2021

Show notes

In this episode I have a wonderful chat with Ronald Schmelzer and Kathleen Walch, authors of "AI Today" the top podcast for those wanting a no-hype, practical, real-world insight into what enterprises, public sector agencies, thought leaders, leading technology companies, pundits, and experts are doing with AI today. Sponsored by Quantum Metric Did you know that 2021 holiday ecommerce sales are expected to exceed 2020 benchmarks? Are you prepared to capture every customer revenue opportunity?  With Quantum Metric, you can be. Visit their website at quantummetric.com/podoffer and see if you qualify to receive their “12 Days of Insights” offer with code DATASCIENCE. This offer gives you 12-day access to the platform coupled with a bespoke insight report that will help you identify where customers are struggling or engaging in your digital product. Sponsored by Amethix Technologies

Links

AI Today podcast: http://aitoday.live/
CPMAI Methodology: https://www.cognilytica.com/cpmai-methodology/

2 effective ways to explain your predictions (Ep. 163) Aug 03, 2021

Show notes

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References

Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. “Model Class Reliance: Variable importance measures for any machine learning model class, from the ‘Rashomon’ perspective.” http://arxiv.org/abs/1801.01489 (2018).
Python SHAP
https://github.com/slundberg/shap

The Netflix challenge. Fair or what? (Ep. 162) Jul 22, 2021

Show notes

Remember the Netflix challenge?

It was a ton of money for the one who would have cracked the problem of recommending the best possible movie.

Was it a fair challenge? Did it work?
Let me tell you what happened...

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Artificial Intelligence for Blockchains with Jonathan Ward CTO of Fetch AI (Ep. 161) Jul 15, 2021

Show notes

In this episode Fetch AI CTO Jonathan Ward speaks about decentralization, AI, blockchain for smart cities and the enterprise.
Below some great links about collective learning, smart contracts in Rust and the Fetch AI ecosystem.

Decentralised collective learning: https://github.com/fetchai/colearn
Smart contracting platform written in Rust https://docs.cosmwasm.com/docs/0.14/
Fetch.ai cosmwasm contracts for collective learning: https://github.com/fetchai/contract-learn
How the Colearn system works: https://vimeo.com/440365943

Apache Arrow, Ballista and Big Data in Rust with Andy Grove RB (Ep. 160) Jul 08, 2021

Show notes

Do you want to know the latest in big data analytics frameworks? Have you ever heard of Apache Arrow? Rust? Ballista? In this episode I speak with Andy Grove one of the main authors of Apache Arrow and Ballista compute engine.
Andy explains some challenges while he was designing the Arrow and Ballista memory models and he describes some amazing solutions.

Our Sponsors

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References

https://arrow.apache.org/

https://ballistacompute.org/

https://github.com/ballista-compute/ballista

GitHub Copilot: yay or nay? (Ep. 159) Jul 06, 2021

Show notes

It made already quite some noise in the news, GitHub copilot promises to be your pair programmer for life.
In this episode I explain how and what GitHub copilot does. Should developers be happy, scared or just keep coding the traditional way?

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Pandas vs Rust [RB] (Ep. 158) Jul 01, 2021

Show notes

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A simple trick for very unbalanced data (Ep. 157) Jun 22, 2021

Show notes

Data from the real world are never perfectly balanced. In this episode I explain a simple yet effective trick to train models with very unbalanced data. Enjoy the show!

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References

Leo Breiman, Random Forests, 2001
C. Chen, A. Liaw, L. Breiman, Using Random Forest to Learn Imbalanced Data (2004)

Time to take your data back with Tapmydata (Ep. 156) Jun 15, 2021

Show notes

In this episode I am with Gilbert Hill, head of strategy at https://tapmydata.com/ We speak about personal data, blockchain and the ability to control it and monetize with another simple yet effective app in the ecosystem. References

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