A deep understanding of deep learning (with Python intro)

Deep learning is increasingly dominating technology and has major implications for society.

From self–driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.

But deep learning is not only about super–fancy, cutting–edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine–learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.

Deep learning is now used in most areas of technology, business, and entertainment. And it’s becoming more important every year.

How does deep learning work?

Deep learning is built on a really simple principle: Take a super–simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.

Is it really that simple? mmm OK, it’s actually a tiny bit more complicated than that 😉   but that’s the core idea, and everything else literally everything else in deep learning is just clever ways of putting together these fundamental building blocks. That doesn’t mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.