Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn

Introduction

Last summer, my family and I visited Russia. Although none of us could read Russian, we had no trouble navigating, thanks to Google's real-time translation of Russian signs into English. This is just one of the many applications of neural networks.

Neural networks are the foundation of deep learning, a subset of machine learning inspired by the structure of the human brain. These networks take in data, train themselves to recognize patterns within that data, and predict outputs for new, similar data.

How Neural Networks Work

To explain how neural networks function, let's create a simple neural network that differentiates between a square, circle, and triangle. Neural networks consist of layers of neurons, which are the core processing units of the network.

Structure of a Neural Network

1. Input Layer: This layer receives the input data.
2. Output Layer: This layer produces the final output.
3. Hidden Layers: These layers perform most of the calculations required by the network.

For instance, consider an image of a circle composed of 28 x 28 pixels, totaling 784 pixels. Each pixel feeds as input into each neuron in the input layer. Neurons from one layer connect to those of the next layer via channels, each assigned a numerical value known as a weight.

The inputs are multiplied by their corresponding weights, and the sum is sent to the neurons in the hidden layer. Each neuron also has a numerical value called bias, added to the input sum. This result is then processed through an activation function, which determines whether the neuron activates and transmits data to the next layer. This process is known as forward propagation.

Once in the output layer, the neuron with the highest value "fires," generating the output. The values essentially represent probabilities. For example, if the neuron associated with the square has the highest probability, that shape is predicted as the output.

Training the Neural Network

At this stage, we must train the network. During training, the actual output is fed along with the input. The predicted output is compared against the actual output to identify prediction errors. The size of the error indicates how wrong the prediction is, while the sign shows whether the predicted values are higher or lower than expected.

The network then transfers this error information backward through the layers during a process called backpropagation. Adjustments to the weights are made based on this feedback. This cycle of forward and backward propagation continues iteratively with multiple inputs until the network can accurately predict the shapes in most cases.

Duration of Training

The training time for neural networks can range from hours to months, but this time investment is often worthwhile considering the wide array of potential applications.

Applications of Neural Networks

Here are some prime applications of neural networks:

• Facial Recognition: Modern smartphones can estimate a person's age based on facial features, thanks to neural networks, which differentiate faces from backgrounds and correlate patterns.
• Forecasting: Neural networks are used to detect weather patterns such as the possibility of rainfall or stock price fluctuations with high accuracy.
• Music Composition: By learning musical patterns, neural networks can even create original compositions.

Keyword

Neural networks, deep learning, machine learning, input layer, output layer, hidden layers, forward propagation, backpropagation, training process, facial recognition, forecasting, music composition.

FAQ

Q1: What is a neural network?
A neural network is a computational model inspired by the human brain's structure, used to recognize patterns in data and make predictions.

Q2: How do neural networks learn?
Neural networks learn by training on data, adjusting weights and biases through a process called backpropagation based on prediction errors.

Q3: What are the main components of a neural network?
The main components of a neural network include the input layer, hidden layers, and output layer.

Q4: How long does it take to train a neural network?
Training a neural network can take from hours to months, depending on the complexity of the model and the amount of data.

Q5: What are some applications of neural networks?
Neural networks are used in various applications, including facial recognition, stock price forecasting, and music composition.