As of the writing of this book i already have 10 deep learning courses

As of the writing of this book I already have 10 deep learning courses

As of the writing of this book, I already have 10 deep learning courses, with more on the way.

There is so much to learn, and even I am still constantly learning.

As I mentioned earlier in this book, deep learning is responsible for producing new state of the art results in both computer vision and NLP, which cover the 2 “fundamental” data types that seem to appear naturally (images and text).

At the same time, unlike the other models we’ve discussed in this lecture, deep learning models are not good with plug and play.

Sure, there is the MLPClassifier, which works on the type of data we looked at in this book, but modern deep learning models are generally more complex.

And so for images and text and other complex data types, you can’t use scikit-learn. Instead, you have to look to more specialized libraries, such as Theano, Tensorflow, and Keras.

In fact, it wasn’t until recently that the MLPClassifier was even included in scikit-learn. There was discussion where developers were against including it, because of the fact that deep neural networks are not really meant to be plug and play like the rest of the models in the scikit-learn library.

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