Deep Learning is a subset of Machine learning. It was developed to have an architecture and functionality similar to that of a human brain. The human brain is composed of neural networks that connect billions of neurons. Similarly, a deep learning architecture comprises artificial neural networks that connect a number of mathematical units called neurons. Deep Learning is capable of modeling complex problems that, in some cases, exceed human performance!
In this you will learn how to create and use a neural network to classify articles of clothing. To achieve this, we will use a sub module of TensorFlow called keras. Before we dive in and start discussing neural networks, I'd like to give a breif introduction to keras. "Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Keras is a very powerful module that allows us to avoid having to build neural networks from scratch. It also hides a lot of mathematical complexity (that otherwise we would have to implement) inside of helpful packages, modules and methods. In this guide we will use keras to quickly develop neural networks. So, what are these magical things that have been beating chess grandmasters, driving cars, detecting cancer cells and winning video games? A deep neural network is a layered representation of data. The term "deep" refers to the ...
A very simple graph that adds two numbers together. In the figure above, two numbers are supposed to be added. Those numbers are stored in two variables, a and b. The two values are flowing through the graph and arrive at the square node, where they are being added. The result of the addition is stored into another variable, c.
The main reason behind deep learning is the idea that, artificial intelligence should draw inspiration from the brain. This perspective gave rise to the "Neural Network" terminology. The brain contains billions of neurons with tens of thousands of connections between them. Deep learning algorithms resemble the brain in many conditions, as both the brain and deep learning models involve a vast number of computation units (Neurons) that are not extraordinarily intelligent in isolation but become intelligent when they interact with each other.
In the figure above, two numbers are supposed to be added. Those numbers are stored in two variables, a and b. The two values are flowing through the graph and arrive at the square node, where they are being added. The result of the addition is stored into another variable, c. Actually, a, b and c can be considered as placeholders. Any numbers that are fed into a and b get added and are stored into c. This is exactly how TensorFlow works. The user defines an abstract representation of the model (neural network) through placeholders and variables. Afterwards, the placeholders get "filled" with real data and the actual computations take place.