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10.07.2020.

Tire Recognition meets Machine learning v.2: Step by step approach – Frank’s case [PART 2]

While in first part about Frank’s case we explained what the problem was, modus operandi and taken steps, here we are going to describe approach conducted and what was the final output and business benefits.
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10.07.2020.

Tire recognition meets Machine Learning v.1: Things to consider and modus operandi

Frank’s tire catalogue included 80 tire manufacturers and over 20 000 unique tire models, with 5digit number of tires moving daily. Aside from the risk of human error during sorting upon arrival, there was a risk of human error when sorting prior to shipping out.
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18.06.2020.

Without a DevOps and Machine Learning, you will fall behind in Industry 4.0

It’s very important for revenue growth to use these technologies, especially because the global business environment is going to be uncertain for a while because of COVID-19. We do not know what consumer spending will look like or how it will bounce back in every industry. You need to maximize the advantages that you have.
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18.06.2019.

Using Q-Learning for Pathfinding

Introduction Reinforcement learning is a part of machine learning that deals with finding the best action to take in a given situation. Unlike supervised learning where the training data is already classified, in reinforcement learning agent does not know if the actions it is about to take are correct or not. Agent must take the action and observe its consequences […]
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