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Special Announcment: Deep Learning Keras Book!

September 12, 2018

Simple Deep Learning for Programmers

Learn Deep Learning via Keras examples with absolutely no math

I’m always intrigued when students tell me they want to learn deep learning without doing any math.

I was explaining to someone just yesterday – if you look at <insert famous deep learning book by famous deep learning researcher here> – the entire thing is actually cover to cover equations. Ha!

Anyhow, I wanted to test this hypothesis. How far can one get, if they try to learn deep learning via an API?

So I made this little book. It’s full of Keras examples, starting from a basic feedforward neural network, then adding some modern techniques like dropout and batch norm, then moving to more advanced architectures like CNNs and RNNs.

Of course, if you are a reader of my newsletter, you probably aren’t afraid of math!

But, I thought I’d share this book with you anyway, since it contains some interesting examples that you haven’t seen in my courses before.

– CIFAR dataset
– time series prediction using an RNN
– machine translation using a Bidirectional RNN (not a seq-to-seq model as in my Advanced NLP course)

This would also be a great opportunity to brush up on your Keras skills, which are going to be useful for my next course (hopefully coming out in a few days!)

Finally – I’ve also linked below my related book, “Simple Machine Learning for Programmers” – it is a similar experiment in teaching about machine learning using an API with no math. It’s the same as the machine learning section of my Numpy course but I know some students like to have written versions of things so they can read on the subway / airplane. If so, check it out!

Get the book now