About this Show
Data Science at Home is a podcast about machine learning, artificial intelligence and algorithms.
The show is hosted by Dr. Francesco Gadaleta on solo episodes and interviews with some of the most influential figures in the field
Listen on:
Episodes
Saturday Sep 26, 2020
Saturday Sep 26, 2020
Come join me in our Discord channel speaking about all things data science.
Follow me on Twitch during my live coding sessions usually in Rust and Python
This episode is supported by Women in Tech by Manning Conferences
Wednesday Sep 16, 2020
Wednesday Sep 16, 2020
Hey there! Having the best time of my life ;)
This is the first episode I record while I am live on my new Twitch channel :) So much fun!
Feel free to follow me for the next live streaming. You can also see me coding machine learning stuff in Rust :))
Don't forget to jump on the usual Discord and have a chat
I'll see you there!
Friday Sep 04, 2020
Friday Sep 04, 2020
In this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.We cover testing with deep learning (neuron coverage, threshold coverage, sign change coverage, layer coverage, etc.), combinatorial testing and their practical aspects.
On September 15th there will be a live@Manning Rust conference. In one Rust-full day you will attend many talks about what's special about rust, building high performance web services or video game, about web assembly and much more.If you want to meet the tribe, tune in september 15th to the live@manning rust conference.
Saturday Aug 29, 2020
Saturday Aug 29, 2020
In this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.
On September 15th there will be a live@Manning Rust conference. In one Rust-full day you will attend many talks about what's special about rust, building high performance web services or video game, about web assembly and much more.If you want to meet the tribe, tune in september 15th to the live@manning rust conference.
Wednesday Aug 12, 2020
Wednesday Aug 12, 2020
After deep learning, a new entry is about ready to go on stage. The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype.This time it's all about privacy and data confidentiality. The new words, homomorphic encryption.
Join and chat with us on the official Discord channel.
Sponsors
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.
References
Towards a Homomorphic Machine Learning Big Data Pipeline for the Financial Services Sector
IBM Fully Homomorphic Encryption Toolkit for Linux
Monday Aug 03, 2020
Monday Aug 03, 2020
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!
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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.
Sunday Jul 26, 2020
Sunday Jul 26, 2020
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.
Wednesday Jul 22, 2020
Wednesday Jul 22, 2020
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.
References
More descent, less gradient
Taylor Series
Sunday Jul 19, 2020
Sunday Jul 19, 2020
In this episode I speak about data transformation frameworks available for the data scientist who writes Python code. The usual suspect is clearly Pandas, as the most widely used library and de-facto standard. However when data volumes increase and distributed algorithms are in place (according to a map-reduce paradigm of computation), Pandas no longer performs as expected. Other frameworks play a role in such context.
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.
References
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/
Friday Jul 03, 2020
Friday Jul 03, 2020
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.
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