Artificial Neural Networks (ANNs) and Deep Learning: An Overview

Artificial Neural Networks (ANNs) and Deep Learning are two related types of machine learning algorithms. Although these terms are often used interchangeably, ANN and Deep Learning are different in terms of their applications. In this article, let’s first understand what Artificial Neural Networks (ANNs). And later, we will discuss Deep Learning and its related aspects.

Artificial Neural Networks (ANNs) are simply a family of models inspired by the human brain which is capable of identifying patterns and performing complex computations

ANNs are a family of machine learning models inspired by the human brain. These models learn to perform complex tasks by considering examples, generally without being programmed with task-specific rules. Instead, they develop their own rules through learning. ANNs have already been trained to accomplish a wide variety of tasks that humans can do—such as recognizing spoken words, identifying images, and making predictions.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection between nodes can transmit a signal from one to another. The receiving node processes the signal and then typically signals downstream nodes connected to it. In common ANN configurations, the signal at a connection between nodes is a real number and the output of each node is computed by some non-linear function of the sum of its inputs. The connections are called edges (or links or lines). Each edge has an associated weight that reflects its relative importance in the computation performed by the neural network.

In order to understand how these networks work, we will first look at the anatomical structure of a typical neuron in our body.

In order to understand how these networks work, we will first look at the anatomical structure of a typical neuron in our body. This will provide a good baseline for what ANNs are and how they are structured.

A human brain contains approximately 100 billion neurons. Each of these neurons can connect with thousands of other neurons through links called synapses (see Figure 4). These connections form an intricate web that guides all the functions carried out by our bodies on a daily basis and even those that we do not realize such as breathing, walking or sleeping. The basic function of a neuron is to receive input signals via its dendrites, process them in its cell body and generate an output signal through its axon which is then transmitted to other neurons via synapse connections. The strength with which two neurons are connected is called weight and this impacts how much impact one neuron has on another neuron’s output. A neuron computes the combined weighted sum of all inputs it receives from connected synapses which determines whether that signal should be passed on or not.

The dendrites collect information from other neurons, this information is passed on to the cell body or soma which processes it. If enough information is received than an electrical signal called action potential is generated which travels along a single output line called axon, which carries it to other neurons.

As a reminder, you can imagine the neuron to be a basic unit in our brain that performs some calculation. The dendrites collect information from other neurons, this information is passed on to the cell body or soma which processes it. If enough information is received than an electrical signal called action potential is generated which travels along a single output line called axon, which carries it to other neurons.Let’s take an analogy, we will have multiple inputs (dendrite) say if we want to know whether it should rain today or not? We will have different inputs such as humidity, temperature and wind speed. The cell body after calculating these pieces of input will decide whether the day will be rainy or not (action potential). For example if the temperature is very low then we can say that there can be good chances of snowfall.

Each neuron acts as a classifier of sorts by generating an output based on its input values. As neural network consists of several neurons that interact with each other, it allows us to fit very complex functions using them.

The basic unit of a neural network is the neuron. A neuron receives input values, processes them using its parameters and generates an output. Each neuron therefore acts as a classifier of sorts by generating an output based on its input values.

A neural network consists of several neurons that interact with each other via their outputs and inputs. This allows us to fit very complex functions by combining the output of several neurons. We will see later how this works in practice with ANNs and Deep Learning.

A simple neural network consists of three layers: Input layer, hidden layer and output layer. We will now see how these neurons fits into each other.

The first layer is the input layer, which is responsible for accepting inputs from the outside world. These inputs are then passed on to a hidden layer, where they are transformed into something that can be understood by the output layer. The output of this process is then sent to the outer world. The number of neurons in each of these layers depends on the complexity of the task. If you are detecting malignant tumors in medical images using deep learning, you could have 1000 input neurons representing 1000 pixel values and 10 output neurons representing 10 classes (1 for malignant and 0 for not malignant). However, if your task was to identify two objects in an image out of 10000 classes (such as dog or cat), you would need 10000 output neurons instead.

Bottom Line

Products that rely on Artificial Neural Networks have already made waves in a whole range of industries, with financial predictions for their commercial value to increase over the next few years. Whether it’s through pattern recognition or the classification of images, ANNs are reshaping how we interact with the world, and the pace of change is accelerating. If you’re looking to tap into this technology, you only have to look at what’s happening in the market right now.