Artificial Intelligence

What is contrastive learning and why it is so powerful? (Ep. 124)

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References
A Simple Framework for Contrastive Learning of Visual Representations
 

Neural search (Ep. 123)

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Let's talk about federated learning (Ep. 122)

Let's talk about federated learning. Why is it important? Why large organizations are not ready yet?
 
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 Monday.com
The Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.
 

How to test machine learning in production (Ep. 121)

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The monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.

Testing in machine learning: checking deeplearning models (Ep. 118)

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.
 
 

Test-First machine learning (Ep. 115)

In this episode I speak about a testing methodology for machine learning models that are supposed to be integrated in production environments.
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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.

GPT-3 cannot code (and never will) (Ep. 114)

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).
 
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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.

[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)

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.
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Protecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106)

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.

Compressing deep learning models: rewinding (Ep.105)

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.
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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 Pruninghttps://arxiv.org/abs/2003.02389