Our Beginner’s Guide to Deep Learning


Do your nerdy friends speak about Artificial intelligence, Machine learning, and Deep learning with ostentatious familiarity? Your desire, in those moments, is to dig a hole and jump into it?

No problem! In this article, we will focus in particular on Deep learning, on its (non-romantic) relationship with Neural networks, and on the essential difference between Deep and “simple” Machine learning.


Are you ready? I will show you how DEEP the rabbit hole goes. You cannot imagine how much.

If someone didn’t understand the quote, well, it’s time to see “Matrix”. After all, you are just 21 years late.


Machine Learning and Deep Learning

Let’s start from the basics. Machine learning is practically a branch of artificial intelligence. It explores the study and construction of algorithms that can learn from data and make predictions about them.

Machine learning is widely applied in computer programming, providing systems the ability to automatically learn and improve from experience without being explicitly programmed.

It can be divided in four main categories, based on different approaches to train our computers: Supervised, Unsupervised, Semi-supervised and Reinforcement.

If you are new to these topics and you want to deepen them, I wrote a super user-friendly article about machine learning and also
another one about its classification. Check them out!

“Machine learning is a branch of artificial intelligence. It explores the study and construction of algorithms that can learn from data and make predictions about them.”


What Is Deep Learning?

So, what about Deep learning? Well, deep learning is subpar of machine learning that mimics the mechanisms of the human brain to process information. Let’s say that it is machine learning’s most advanced version. Later, i’ll show you why!

Understood? Deep learning is a part of machine learning that is a part of artificial intelligence. Deep-ception!

Although deep learning was first theorized in the 1980s, it has only recently shown extraordinary developments.


Why Now?

There are essentially two main reasons for this rapid evolution.

Firstly, deep learning requires large amounts of labeled data to train our machine. Labeled data is data that have been tagged with one or more informative labels.

Translated: it’s data associated with useful information, like a picture of a kitty labeled with the label “cat”.

In the last years, we have been able to produce, store and manage a growing amount of data. For example, developing driverless cars requires thousands, if not millions of pictures and countless hours of video.

Secondly, deep learning requires impressive computing power. Today, we can solve this problem with High-performance GPUs, combined with cloud computing services. In such a way, it’s possible to reduce the training time for a deep learning network from weeks to hours or less.


Deep Learning and Neural Networks

Deep Learning is an approach to Machine Learning based on neural networks. A neural network is a set of task-specific algorithms that are inspired by the structure and function of the human brain.

It consists of interconnected layers of units, called artificial neurons, which send data to each other though connections called edges. The output of the preceding layer will be the input of the subsequent layer.

The first and the last layer of the network are called respectively input layer and output layer. Each layer between these is known as a “hidden layer”.

Signals travel from the input layer to the output layer, possibly after traversing them multiple times. Every layer takes care of recognizing different features of the overall data.

An attached “weight” is associated with each link between layers, the value of which can be increased or decreased to alter the importance of that link. At the end of each training cycle, the system will examine wh