Saidi Souhaieb

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# What are Neural Networks? Deep Learning Explained for Beginners

Intrigued by the idea of computers learning? well Neural Networks are the main key players, they are interconnected neurons that resemble loosely the structure of a human brain, mimicking the act of learning and decision making.

In this guide, you’ll learn:

• What are Neural Networks
• What are Neural Networks made of
• How do Neural Networks learn
• What are the types of Neural Networks

## What are Neural Networks

Artificial Neural Network or (ANN) is a deep learning method in artificial intelligence based on the human brain, made to make a computer capable of learning to perform tasks based on training examples. However, for a computer to be able to perform these tasks it has to learn, which is the key concept behind neural networks, for instance, a neural network may be fed thousands of images of cats and dogs each labelled with “cat” or “dog”, and it would find visual patterns in the images to learn how to classify a given image as a cat or a dog.

## What are Neural Networks made of

In broad terms, layers are the building blocks of neural networks made of nodes, each responsible for processing and transforming data in a specific way. There are three types of layers:

1. Input Layer: This is layer receives the input data and pass it to he rest of the network.
2. Hidden Layer: Made of one or more layers based on the complexity of the network, they are the key components of learning and complexity. Hidden layers work by analyzing the output from the previous layer, processing it further and passing it on the next layer.
3. Output Layer: Holds the final results of the network after all the data processing. The number of nodes of this layer depends on the number of classes to be predicted.

Diving a little bit deeper to the connection between two layers, we find that each node is connected to every node in the next layer. The strength of these connections is determined by a parameter called weight, during training, the network learns the optimal values for these weights through a process called backpropagation.

## How do Neural Networks learn

Learning or mostly known as Training, generally speaking it is a process involving presenting neural networks with examples of input data along with the corresponding correct output. Specifically multiple steps are involved in the training process. These steps include:

1. nitialization: Setting up the initial configuration of the neural network, which typically involves assigning random values to its parameters such as weights.
2. Forward Propagation: Processing input data through the network to generate predictions or outputs.
3. Error Measurement: Assessing the accuracy between the predicted outputs and the actual outputs using a measure of error or loss.
4. Adjustment Calculation: Determining how to update the network’s parameters based on the observed errors, aiming to reduce them over time.
5. Parameter Updates: Modifying the parameters of the network, such as weights and biases, to minimize the errors observed during training.
6. Iterative Optimization: Repeating the above steps across multiple iterations, gradually improving the network’s performance.
7. Validation and Testing: Assessing the trained network’s performance on independent validation and test datasets to ensure its ability to generalize to unseen data.

## What are the types of Neural Networks

Neural Networks are varied, each with a unique structure and function to solve different problems. The most common neural networks are:

• Convolutional neural network(CNN): Used in classification and computer vision tasks, it utilizes filters optimization to reduce images into a form that is easier to process, without losing critical features. Real life applications include pattern recognition, image recognition, object detection, facial recognition and more.
• Recurrent neural networks(RNN): A bi-directional neural network for handling sequential data, meaning RNNs have connections that form directed cycles where output from the previous step is fed as input to the current step. Used in cases like speech recognition or handwriting recognition where it is essential to remember previous information.

## Final Thoughts

In this beginner-friendly guide, i explained neural networks on a broader level avoiding complex explanations, concepts and mathematical equations. For more mathematically involved discussion refer to the references.

References: