If you’re a software engineering enthusiast trying to get an understanding of how neural networks work, you’re in the right place.
In this guide, we will help beginners learn the meaning of neural networks, get an introduction on what a neural network model is, and expand their knowledge to new areas of the field.
What is a Neural Network?
Before getting into computational terminology, let us understand the existence of neural networks in our everyday lives.
The term “neural” comes from “neuron”, which is the term used for a single nerve cell. That’s right – a neural network essentially means a network of neurons that perform simple and actions in our daily lives.
Pattern recognition, object detection, and intelligence are a major aspect of the problems we face every day. While they are performed with so much ease that we don’t even realize, the truth is that these reactions are difficult to automate.
- Children memorizing what an apple looks like
- An animal recognizing its mother or owner
- Perceiving whether something is hot or cold
Our neural networks perform these complicated computations.
Humans have now been able to build a computation system that can perform in a manner similar to our nervous system. These are called artificial neural networks (ANNs).
While we used the ANNs initially to perform simple functions, the increase in computation power has now allowed us to build a rather powerful neural network architecture to solve increasingly complicated problems.
Let’s learn about ANN in-depth in the next section.
What is an Artificial Neural Network Model?
A multi-layer, fully-connected neural network containing an input layer, hidden layers, and an output layer is called an artificial neural network or ANN.
The image below represents an ANN.
If you see carefully, you will notice that each node in one layer is connected to every node in the layer next to it.
As you increase the number of hidden layers, the network becomes deeper.
Let’s see what an individual node in the output or hidden layer looks like.
As you can see, the node gets many inputs. It sums up all the weights and passes it on as output, via a non-linear activation function.
This output of the node becomes the input of the node in the next layer.
An important thing to note here is that the signal will always move from left to right. Once all the nodes have followed the procedure, the final output will be given.
Here’s what the equation of a node looks like.
In the above equation, b is bias. It is the input to all nodes and always carries the value 1.
Bias helps to move the activation function result to left or right.
Glossary of Artificial Neural Network Model
Let’s look at the basic terms you should know when it comes to an artificial neural network model.
The data first fed into the neural network from the source is called the input. Its goal is to give the network data to make a decision or prediction about the information fed into it. The neural network model usually accepts real value sets of inputs and it should be fed into a neuron in the input layer.
The inputs for which you already know the correct outputs are called training sets. These are used to help the neural network get trained and memorize the result for the given input set.
Every neural network generates an output as a prediction or decision about the data fed into it. This output is in the form of real values set or Boolean decisions. Only of the neurons in the output layer generates the output value.
Also known as a perceptron, a neuron is the basic unit of a neural network. It accepts an input value and generates an output based on it.
As discussed before, every neuron receives a part of the input and passes it through the non-linear activation function to the node in the next layer. These activation functions can be TanH, sigmoid, or ReLu. The non-linear feature of these functions helps to train the network.
Every neuron has a numeric weight. When it delivers input to another note, its weight is totaled with the others to generate an output. By making small changes to these weights are how neural networks are trained. The fine-tuning of weights helps determine the correct set of weights and biases that would generate the best outcome. This is where backpropagation comes in.
What is Backpropagation in a Neural Network Model?
One of the ways to successfully find out the small changes that need to be made to the weights to minimize the loss of the entire network is backpropagation.
- At first, the activations are to be propagated in the upward or feedforward direction.
- Now, the cost function derivatives have to be propagated in the downward or reverse direction.
This way, you will be able to determine the partial cost derivative against each weight. You can then compute the cost that would be reduced by making the adjustments.
A lot of software engineers do not recommend the neural network model, as they feel that it is rather inefficient, given that several iterations are needed to come up with the most cost-effective solution.
However, numerous new algorithms, such as Hinton’s capsule networks, capsule neural network, require much fewer instances of adjustments to reach a precise model. And so, surely neural networking has a lot of scope in the future.
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Why do you need to study math for artificial intelligence?
Contrary to what many of us think, artificial intelligence is highly dependent on mathematics. The whole concept of teaching machines to think and act similar to human beings is based on concepts that belong to different branches of mathematics, like probability and statistics, to name a few. Data science also comes with its underpinnings related to various mathematical concepts, from calculus, linear algebra, and game theory to advanced regressions, classifications, statistics, probability, gradient descent, and more. Strong fundamentals in mathematics are essential for developing an effective understanding of AI concepts, which will help you build a successful career in this field.
Is neural network an algorithm in machine learning?
Artificial neural networks or simply, neural networks, can be defined as a set of machine learning algorithms designed to resemble the human brain and created for advanced pattern recognition. Neural networks are designed to identify numerical patterns within vectors into which all the accumulated data are translated. By detecting patterns within unstructured or imprecise data, neural networks help to classify and form clusters for improved data storage and management. Sensory data that is fed to neural networks are interpreted via a machine perception that labels or clusters the input data.
Why is backpropagation needed in neural networks?
In the realm of artificial neural networks, the backpropagation method actually refers to the backward propagation of errors. This is a standardized technique used to train artificial neural networks and goes by iterations. Backpropagation is used to fine-tune the weights of an artificial neural network, help minimize errors and make the system more accurate and reliable. This method works quickly and can also be programmed simply. It is a flexible technique; it does not need previous knowledge about the neural network. It involves no extra parameters but only tunes the numbers fed into the system.