I’m launching an instance in compute engine and I’m surprised to see that adding a Tesla T4 GPU to an instance does not change the estimated cost at all. The GPU doesn’t even show up in cost details.

Here’s a screenshot: https://i.imgur.com/wqhthS1.png

This seems super weird to me. Is it a bug?

Context : I am a business professional who recently joined an AI startup on voice.

As my first project, I am tasked with understanding patent law better to package solutions we have built as potential patents.

To do this, I am taking this approach.

• Research as much as possible about patent law (stuck here)
• formulate solutions basis our ml capabilities
• file eventually for patents.

What can be patented in artificial intelligence have very vague answers and differ everywhere.

Any help you can provide as a starting point, information I could use or a better approach is appreciated.

I understand that asking for an “originality checking” neural network is very vague since there are many parameters which can affect the originality of an image, but I’m curious.

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I was thinking the Siamese network approach might be beneficial. What do you think?

So I’ve worked with machine learning a lot, but never actually had to deal directly with probabilities/probability output, and what they actually mean in a real world scenario.

So, say we have 4 balanced classes [1,2,3, and 4], and we train a Keras classifier and get the probabilities for a given prediction in the test set:

GT: Class 1

Output: [0.4, 0.3, 0.1, 0.2]

Great – the classifier predicted Class 1 correctly. But how should we interpret the 0.4 probability that was attached to the class?

Does it mean that given that particular feature vector, class 1 will be the correct choice 40% of the time?

Where does the actual performance/accuracy of the classifier come in to this?

For example, say the above classifier was trained with 100k training examples, had an accuracy of ‘50%’, and produced the probabilities of [0.4, 0.3, 0.1, 0.2]. Now imagine I got more training data and trained with 500k training examples, achieved an accuracy of 60%, and still got the probabilities of [0.4, 0.3, 0.1, 0.2]. Does the more accurate classifier better represent the actual real-life probability of the events?

tl;dr: I guess what I’m actually asking is – how do we know/measure/compare the accuracy of the probabilities given from a classifier?

If you’re interested on taking your image recognition solution to the next level efficiently using GPUs/TPUs, multi-threading and distributed computing, you may need to reorganize your processing.

Watch this introductory video on how to achieve this and you may get 4X performance improvements!!!

https://youtu.be/JvssZESVcjI

the arXiv link to the paper is included!!!

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