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Data Sets for Machine Learning Model Training

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Deep learning and other machine learning techniques have rapidly become a transformative force in computer vision. Compared to conventional computer vision techniques, machine learning algorithms deliver superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence, which had been solved quite well with conventional techniques, are now finding even better solutions using machine learning techniques. The pace of ongoing machine learning development remains rapid. And in comparison to traditional computer vision algorithms, it's easier to create effective solutions for new problems without requiring a huge team of specialists. But machine learning is also resource-intensive, as measured by its compute and memory requirements. And for machine learning to deliver its potential, it requires a sufficient amount of high quality training data, plus developer knowledge of how to properly use it.

Imagine teaching someone how to bake a cake. You can do it by writing down instructions, or you can show them how it’s done and have them learn by example. This, in a nutshell, is the difference between traditional coding and machine learning: coding is like writing a recipe, whereas machine learning is teaching by example. If you teach cake baking by example, you'll probably have to demonstrate the process many times before your student remembers all the details and can bake a cake on their own from memory. The process of baking a cake is conversely easy to describe with a recipe, but many other problems are very difficult to solve this same "recipe way."

For example, how would you teach someone to discern between an image of a cat and an image of a dog? It would be practically impossible to write a “recipe” for cat-vs-dog classification; alternatively, though, with enough example images you could teach this skill in straightforward fashion. Many problems in computer vision and artificial intelligence that are impractical to solve with traditional coding and algorithms become solvable with deep learning or some other form of machine learning.

You can think of DNNs (deep neural networks) as universal approximators: they can be structured to map any input to any output (e.g., the input can be a picture, and the output can be the probabilities that the picture depicts a dog and a cat, respectively). When trained properly, they can provide a very good approximate solution to just about any problem you throw at them, under the important assumption that the network topology is a sufficiently effective...