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open source platform for machine learning

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Mode on: Comparing the two best colorization AI's

Reading time11 min

This article continues a series of notes about colorization. During today's experiment, we’ll be comparing a recent neural network with the good old Deoldify to gauge the rate at which the future is approaching.

This is a practical project, so we won’t pay extra attention to the underlying philosophy of the Transformer architecture. Besides, any attempt to explain the principles of its operation to a wide public in hand waving terms would become misguiding.

A lecturer: Mr. Petrov! How does a transformer work?
Petrov with a bass voice: Hum-m-m-m.

Google Colorizing Transformer vs Deoldify

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Coins classifier Neural Network: Head or Tail?

Reading time14 min

Home of this article: https://robotics.snowcron.com/coins/02_head_or_tail.htm

The global objective of these articles is to build a coin classifier, capable of scanning your pocket change and find rare / valuable coins. This is a second article in a series, so let me remind you what happened earlier (https://habr.com/ru/post/538958/).

During previous step we got a rather large dataset composed of pairs of images, loaded from an online coins site meshok.ru. Those images were uploaded to the Internet by people we do not know, and though they are supposed to contain coin's head in one image and tail in the other, we can not rule out a situation when we have two heads and no tail and vice versa. Also at the moment we have no idea which image contains head and which contains tail: this might be important when we feed data to our final classifier.

So let's write a program to distinguish heads from tails. It is a rather simple task, involving a convolutional neural network that is using transfer learning.

Same way as before, we are going to use Google Colab environment, taking the advantage of a free video card they grant us an access to. We will store data on a Google Drive, so first thing we need is to allow Colab to access the Drive:

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Top 5 Reasons to Learn TensorFlow

Reading time4 min

Top 5 Reasons to Learn TensorFlow

Artificial Intelligence or AI has gone far beyond its beginning and self-driving cars, virtual assistants, Google Lens, and personalized marketing, are some of the initial powerful applications of AI. The present generation is yet to witness the true potential of AI in the years to come. Companies are investing huge amounts of money to leverage the power of AI and build applications that offer best-in-class solutions to real-world problems.

Broadly speaking, AI is one of the powerful fields of computer science which focuses on making machines capable of mimicking human actions. Such machines are not explicitly programmed and it learns from past experiences to make decisions quite similar to what humans do through their brain. When one starts exploring AI, it is inevitable to come across its subset i.e. Machine Learning. Further deep learning is a subset of machine learning.

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