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Michał Frącek
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  • Warsaw
  • Poland
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Michał Frącek posted a blog post

Data Quality Case Studies: How We Saved Clients Real Money Thanks to Data Validation

Machine learning models grow more powerful every week, but the earliest models and the most recent state-of-the-art models share the exact same dependency: data quality. The maxim “garbage in – garbage out” coined decades ago, continues to apply today. Recent examples of data verification shortcomings abound, including JP Morgan/Chase’s 2013 fiasco and this lovely list of …See More
Jul 5
Michał Frącek's blog post was featured

Data Quality Case Studies: How We Saved Clients Real Money Thanks to Data Validation

Machine learning models grow more powerful every week, but the earliest models and the most recent state-of-the-art models share the exact same dependency: data quality. The maxim “garbage in – garbage out” coined decades ago, continues to apply today. Recent examples of data verification shortcomings abound, including JP Morgan/Chase’s 2013 fiasco and this lovely list of …See More
Jul 5
Ante Bilic liked Michał Frącek's blog post Recognizing Animals in Photos: Building an AI model for Object Recognition
Jun 27
Michał Frącek's 2 blog posts were featured
Jun 26
Michał Frącek posted a blog post

Recognizing Animals in Photos: Building an AI model for Object Recognition

Our model for recognizing specific animals in images is a neural network consisting of multiple layers, and the initial layers are already good at understanding the world in general. So instead of “re-inventing the wheel,” we only need to train the final layers.I was excited to work on a recent project with one of our partners, Wild Detect, because it aligns with one of our goals at Appsilon — to use data science consulting to aid in the preservation and management of our planet’s wildlife and…See More
Jun 26
Tauheedul Ali liked Michał Frącek's blog post “Please, explain.” Interpretability of black-box machine learning models
Apr 25
Michał Frącek posted a blog post

“Please, explain.” Interpretability of black-box machine learning models

In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. It’s one of the direct consequences of implementing GDPR in EU. This means that a bank needs to be able to explain why the loan wasn’t granted if the decision process was automatic.In October 2018 world headlines reported about …See More
Apr 18
Michał Frącek's blog post was featured

“Please, explain.” Interpretability of black-box machine learning models

In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. It’s one of the direct consequences of implementing GDPR in EU. This means that a bank needs to be able to explain why the loan wasn’t granted if the decision process was automatic.In October 2018 world headlines reported about …See More
Apr 18

Profile Information

Short Bio
Growth expert @Appsilon
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/micha%C5%82-fr%C4%85cek-1bb73650/
Field of Expertise
Data Science, Business Analytics
Professional Status
Consultant
Your Company:
Appsilon Data Science
Your Job Title:
Growth Consultant
Interests:
Contributing

Michał Frącek's Blog

Data Quality Case Studies: How We Saved Clients Real Money Thanks to Data Validation

Posted on July 4, 2019 at 4:21am 0 Comments

Machine learning models grow more powerful every week, but the earliest models and the most recent state-of-the-art models share the exact same dependency: data quality. The maxim “garbage in – garbage out” coined decades ago, continues to apply today. Recent examples of data verification shortcomings abound, including JP Morgan/Chase’s 2013 fiasco and this lovely…

Continue

Recognizing Animals in Photos: Building an AI model for Object Recognition

Posted on June 25, 2019 at 1:13am 0 Comments

Our model for recognizing specific animals in images is a neural network consisting of multiple layers, and the initial layers are already good at understanding the world in general. So instead of “re-inventing the wheel,” we only need to train the final layers.

I was excited to work on a recent project with one of our partners, Wild Detect, because it aligns with one of our goals at Appsilon — to use data science consulting to aid in the…

Continue

“Please, explain.” Interpretability of black-box machine learning models

Posted on April 17, 2019 at 7:30am 0 Comments

In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. It’s one of the direct consequences of implementing GDPR in EU. This means that a bank needs to be able to explain why the loan wasn’t granted if the decision process was automatic.

In October 2018 world headlines reported about …

Continue

Summary from Gartner Data & Analytics Summit London 2019

Posted on March 20, 2019 at 4:00am 0 Comments

Last week I attended a three-day Gartner Summit in London. For all those who didn’t manage a trip, I want to share with you the biggest insights I gathered from the conference and review those things that impressed me the most.

Business Vibe

Visualization and interaction go hand in hand in modern businesses. Regardless of industry, the vibe, energy, and atmosphere defines a brand. And at the Gartner Summit, that vibe was entertainment with a…

Continue

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