Neural networks are great for complex data sets, but some sets have more features to figure out than others. Many times these features are initialized based on heuristics and they have to be tuned as the model returns predictions. With convolutional neural networks, the model tunes the features for itself.
In this talk, you will learn some use cases for CNNs, how they work under the hood, and how you can create a CNN in Python. You’ll be able to see how convolutions and max-pooling help decrease the amount of pre-processing you have to do. By the end of the talk, you should have a good understanding of the basics of CNNs and how to implement them.
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