How to Understand Artificial Neural Networks: Complete Step by Step Guide
By Braincuber Team
Published on April 24, 2026
Artificial Neural Networks (ANN) are the most popular machine learning algorithms today. This complete step by step beginner guide covers everything you need to know about ANN, from basic architecture to advanced applications in real-world scenarios.
What You'll Learn:
- What is Artificial Neural Network and how it mimics the human brain
- The three essential layers of ANN: input, hidden, and output
- How activation functions and backpropagation work
- Difference between FeedForward and Feedback neural networks
- Understanding Bayesian Networks and their applications
- Real-world applications of ANN in various industries
What is ANN?
Artificial Neural Networks are a special type of machine learning algorithms that are modeled after the human brain. That is, just like how the neurons in our nervous system are able to learn from the past data, similarly, the ANN is able to learn from the data and provide responses in the form of predictions or classifications.
ANNs are nonlinear statistical models which display a complex relationship between the inputs and outputs to discover a new pattern. A variety of tasks such as image recognition, speech recognition, machine translation as well as medical diagnosis makes use of these artificial neural networks.
An important advantage of ANN is the fact that it learns from the example data sets. Most commonly usage of ANN is that of a random function approximation. With these types of tools, one can have a cost-effective method of arriving at the solutions that define the distribution. ANN is also capable of taking sample data rather than the entire dataset to provide the output result.
Artificial Neural Networks Architecture
The functioning of the Artificial Neural Networks is similar to the way neurons work in our nervous system. The Neural Networks go back to the early 1970s when Warren S McCulloch and Walter Pitts coined this term. In order to understand the workings of ANNs, let us first understand how it is structured. In a neural network, there are three essential layers.
Input Layers
The input layer is the first layer of an ANN that receives the input information in the form of various texts, numbers, audio files, image pixels, etc. This layer is responsible for passing the raw data to the subsequent layers for processing.
Hidden Layers
In the middle of the ANN model are the hidden layers. There can be a single hidden layer, as in the case of a perceptron or multiple hidden layers. These hidden layers perform various types of mathematical computation on the input data and recognize the patterns that are part of it.
Output Layer
In the output layer, we obtain the result that we obtain through rigorous computations performed by the middle layer. This layer produces the final predictions or classifications based on the learned patterns.
Key Parameters in Neural Networks
In a neural network, there are multiple parameters and hyperparameters that affect the performance of the model. The output of ANNs is mostly dependent on these parameters.
| Parameter | Description | Role in ANN |
|---|---|---|
| Weights | Numerical values assigned to each node | Determine strength of connections between neurons |
| Bias | Additional constant added to input | Helps adjust the output along with weights |
| Learning Rate | Step size during weight adjustment | Controls how quickly the network learns |
| Batch Size | Number of samples processed together | Affects training speed and memory usage |
| Transfer Function | Calculates weighted sum of inputs and bias | Computes node input value |
| Activation Function | Determines if node should fire | Introduces non-linearity to the network |
Popular Activation Functions
After the transfer function has calculated the sum, the activation function obtains the result. Based on the output received, the activation functions fire the appropriate result from the node. For example, if the output received is above 0.5, the activation function fires a 1 otherwise it remains 0.
Sigmoid Function
Maps values to range 0-1. Commonly used in output layers for binary classification problems.
RELU (Rectified Linear Unit)
Returns 0 for negative values, and the value itself for positive. Most widely used activation function in deep learning.
Softmax Function
Converts logits to probabilities that sum to 1. Used in multi-class classification output layers.
Tanh Function
Maps values to range -1 to 1. Zero-centered, making it easier to model inputs with negative values.
Back Propagation in Artificial Neural Networks
In order to train a neural network, we provide it with examples of input-output mappings. Finally, when the neural network completes the training, we test the neural network where we do not provide it with these mappings. The neural network predicts the output and we evaluate how correct the output is using the various error functions.
Finally, based on the result, the model adjusts the weights of the neural networks to optimize the network following gradient descent through the chain rule. This process is known as backpropagation.
ANNs are part of an emerging area in Machine Learning known as Deep Learning.
Types of Artificial Neural Networks
FeedForward Neural Network
In the feedforward ANNs, the flow of information takes place only in one direction. That is, the flow of information is from the input layer to the hidden layer and finally to the output. There are no feedback loops present in this neural network. These type of neural networks are mostly used in supervised learning for instances such as classification, image recognition etc. We use them in cases where the data is not sequential in nature.
Feedback Artificial Neural Networks
In the feedback ANNs, the feedback loops are a part of it. Such type of neural networks are mainly for memory retention such as in the case of recurrent neural networks. These types of networks are most suited for areas where the data is sequential or time-dependent.
Bayesian Networks
These type of neural networks have a probabilistic graphical model that makes use of Bayesian Inference for computing the probability. These type of Bayesian Networks are also known as Belief Networks.
In these Bayesian Networks, there are edges that connect the nodes representing the probabilistic dependencies present among these type of random variables. The direction of effect is such that if one node is affecting the other then they fall in the same line of effect.
The only constraint that these networks have to follow is it cannot return to the node through the directed arcs. Therefore, Bayesian Networks are referred to as Directed Acyclic Graphs (DAGs).
With the help of Bayesian Networks, one can combine the prior knowledge as well as the observed data. Bayesian Networks are mainly for learning the causal relationships and also understanding the domain knowledge to predict the future event. This takes place even in the case of missing data.
Artificial Neural Networks Applications
Artificial Neural Networks have numerous real-world applications across various industries. Here are the most prominent ones:
Handwritten Character Recognition
ANNs are used for handwritten character recognition. Neural Networks are trained to recognize the handwritten characters which can be in the form of letters or digits.
Speech Recognition
ANNs play an important role in speech recognition. With the advent of deep learning, various types of neural networks are the absolute choice for obtaining an accurate classification.
Signature Classification
For recognizing signatures and categorizing them to the person's class, we use artificial neural networks for building these systems for authentication.
Facial Recognition
In order to recognize the faces based on the identity of the person, we make use of neural networks. Convolutional Neural Networks are the most popular type of ANN used in this field.
Financial Forecasting
ANNs have shown great performance in financial forecasting. Neural networks are capable of foretelling stock prices so that investors may make appropriate decisions.
Healthcare Diagnosis
In the field of health care, ANNs are used for diagnosis of ailments. Neural networks contribute to increased diagnostic accuracy and early treatment.
Summary
Artificial Neural Networks (ANN) are inspired by how the human brain works. They are made up of layers of nodes or neurons that work together to solve complex problems. Each node gets information, processes it, and passes it to the next. ANNs are used in image recognition, speech processing, and natural language understanding.
ANNs have three main layers: input, hidden, and output. The input layer takes data. Hidden layers perform calculations. The output layer gives the result. The network adjusts weights and biases using training data. This process is called backpropagation. It helps the model learn from mistakes and get better over time.
ANNs are used in many modern technologies. They help phones understand voice commands, help doctors detect diseases, and help banks find fraud.
Frequently Asked Questions
What is an Artificial Neural Network?
An Artificial Neural Network (ANN) is a machine learning algorithm modeled after the human brain. It consists of layers of interconnected nodes that process data and learn from patterns to make predictions or classifications.
What are the three layers of ANN?
The three essential layers are: Input Layer (receives data), Hidden Layer (performs computations and pattern recognition), and Output Layer (produces final predictions).
What is backpropagation in neural networks?
Backpropagation is the process of adjusting neural network weights based on prediction errors. It uses gradient descent and the chain rule to minimize the error between predicted and actual outputs.
What is the difference between FeedForward and Feedback neural networks?
FeedForward networks only allow one-way data flow from input to output. Feedback networks include feedback loops and are used for memory retention and sequential data processing.
What are the main applications of ANN?
ANNs are used in handwritten character recognition, speech recognition, signature classification, facial recognition, financial forecasting, and healthcare diagnosis.
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