Dissecting Deep Learning

Deep learning. Pretty much everything you hear about AI in the news today is in some way related to this term. What is deep learning? How does it work? Where can it be used? And how will it be developed in the future?

In this Collection, I’m trying to decompose a massively exploding field into a logical structure to make it comprehensible.

I’ll first cover the general aspects of deep learning, or deep neural networks, if you like. That includes a little bit of history, the components of deep neural networks and how you train them.

I will then dive deeper into various types of neural networks. So far, I’m planning to cover Convolutional Neural Networks, which can be used for image-like data, as well as Recurrent Neural Networks, which are primarily used for timeseries data (although the first can be used as well).

This is followed by a wide array of application types for deep learning models. Finally, based on various sources, I will be trying to construct a set of viewpoints about where people think deep learning is heading to.

I’ve done my utmost best to write in comprehensible English. Since I’m originally not a native speaker, it may unfortunately be the case that I’ve written things down in a weird way or, worse, in a way you don’t understand. In that case, please let me know ASAP so I can correct this. we Dutchies don’t mind directness, so please don’t hesitate and leave a comment below 👇😉

Please note that this is a work in progress. I’m working as hard as I can to get it done ASAP, but it takes time. In order to avoid that I can only publish when it is finished in its entirety (which may or may not happen), I’ve already published what I have so far. If you think it’s interesting, please feel invited to add this page to your bookmarks and check back every now and then. I promise that I’ve done my best to generate new content by then!

Deep neural networks in general

Weight initialization

Activation functions

Loss and loss functions

Optimizing your model

Regularization and Normalization

Configuring the Training Process

  • Batch size.
  • Epochs.

Convolutional Neural Networks

Recurrent Neural Networks


Generative models

Application Types for Deep Learning

The future of deep learning

Work in progress.