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I tried this approach to learn ML but failed to understand how to implement the transfer learning and using the CNN model on a non-image data. A: I agree with you that the accepted answer does not explain in enough detail what exactly is going on. There are two things you need to know for this problem: What is classification? What is transfer learning? Let's start with 1. A "classification" problem is when you have a set of "training samples" where each sample is an object with a set of "attributes" or "features" that describe it. In this case the attributes are the pixels in the image and the "class" is whatever the input image actually shows. You want to use the trained CNN (if you already have one that is good enough) to make predictions for new samples where the sample only has the attributes of the training samples (not the image). The "transfer learning" concept has to do with using a pre-trained CNN to predict a set of new samples that are from the same domain as the training samples. As for 2, the following is a very good description of what transfer learning is. In my opinion the highest-quality explanation is this, but I also recommend you watch this video. If you read through this article you will quickly see what transfer learning is about. MAZDA2 The Mazda2 (known as the Mazda CX-2 in China) is a small subcompact car from the Japanese manufacturer Mazda. Launched as the fourth generation of the Mazda2 in 2015 and the first in over a decade, the car is a mild update to the previous generation Mazda2/2 Mazda2i, which was launched in 2002 and discontinued after the 2009 model year. Although launched worldwide as a four-door sedan, the Mazda2 is built in Mazda's Nagare plant in Hiroshima and sold in North America, Australasia and Japan as a two-door hatchback. In Japan and the United States, the Mazda2 is marketed as the subcompact CX-2 and in Europe, the car was originally sold as the Mazda2 EX and later the Mazda2 SE. In Malaysia, the Mazda2 is marketed as the sedan version and





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