# 5 beginner friendly steps to learn neural network tutorial

A neural network mimics the human brain. The system architecture is made of artificial neurons and such a network can perform multiple functions in different industries. If you consider a career in the field of machine learning and neural networks, then a neural network tutorial is a must. You can start with a beginner-friendly tutorial and then move on to advanced topics of study. The AIML from Imarticus Learning is ideal for those interested in becoming specialists in the field.

A Guide to Neural Network in 5 Steps

To understand a neural network, you need to understand the workings of such a network. If you opt for a Masters's in artificial intelligence that includes a specialization in neural networks, it will be easier for you to grasp the concept and become an expert.

A neural network has three distinct layers: the input layer, the hidden layer, and the output layer. Before we get into the details of the neural network tutorial, you need to understand how each of these layers functions. Now each layer is comprised of nodes and there can be more than one hidden layer.

As the name suggests, the input layer is responsible for recognizing and taking inputs, before transferring the signals to the next layer. Now, the hidden layers are where the back-end calculations occur. Once the results are obtained, the output layer transmits them.

Now that you know the workings of each layer, it is important to take a look at how the network functions. Here are 5 steps that are involved in the working of a neural network.

Step 1: Information Enters the Input Layer and Assignment of Weights

The data or the information is fed into the input layer. This then passes on to the hidden layer. At this interconnection, weights are assigned to every input.

The weights will multiply with each individual input. Once that happens, a bias is added to every input.

Step 3: Transfer of Weighted Sum and Activation Function

The weighted sum, once obtained transfers onto the activation function. It is the activation function that decides which of the nodes can be used for the extraction of specific features.

Step 4: Application Function

For the output layer to deliver, the deployment of an application function is necessary. It prompts the output layer to generate the output metrics.

Step 5: Back-Propagation of Output

The weights need to be adjusted and then the output result is back-propagated. This helps to reduce errors.

Using the above 5 steps, you can implement neural networks to approximate multiple functions accurately. To learn more about neural networks and move beyond the beginner level, you can opt for a course from Imarticus Learning.

Learn Neural Networking from Imarticus Learning

Imarticus Learning offers certification in Artificial Intelligence and Machine Learning. We have designed this particular program with academicians and industry experts from the E&ICT Academy and IIT Guwahati. If you have a Bachelor's or a Master's degree in computer science, statistics, mathematics, economics or science and engineering with at least 50% in your graduation, then you are eligible for this course.

Our Artificial Intelligence and Machine Learning program include specialized topics like AI deep learning, machine learning, data science, and data analytics. Once you complete the course you will be able to seek job opportunities in all of these disciplines.

The mode of training for this course is online and we organize live lectures every week. You will spend 8 hours every week learning from the best academicians and professionals. We encourage students to interact and build networks during these sessions. At Imarticus Learning, we also provide hands-on training through 25 real-world business projects and more than 100 assignments.

If you are interested in the current implementation of neural networks and wish to build a career in it, our certificate program is one of the best options. You can choose Imarticus Learning to gain excellent experience and engage with industry experts.

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