Neural Networks Demystified: Understanding How They Work

Neural networks become increasingly popular in recent years. They are a type of machine learning model that has the ability to learn and improve upon its performance over time. Neural networks are made up of interconnected nodes that work together to analyze data and make predictions. In this article, we’ll dive into the world of neural networks and explore what they are, how they work, and their applications.

What is a Neural Network?

A neural network is a machine learning model that is inspired by the structure and function of the human brain. It consists of interconnected nodes (called neurons) that are organized into layers. The input layer receives data and passes it through the network, with each subsequent layer performing calculations and passing the result to the next layer until a final output is produced. This means the output of one layer will be the input for the next layer.

How Do Neural Networks Work?

Neural networks work by learning from data. During the training process, the network adjusts the weights between the neurons to improve its performance. The weights determine the strength of the connections between the neurons and are updated during each iteration of the training process. This process continues until the network’s performance reaches the desired level.

Layers

Layers are used to organize the neurons of the network into groups, and they play a critical role in the network’s ability to learn and make predictions.

Mainly we classify layers into three types,

1. Input layer: This is the first layer of the network, The number of neurons in the input layer is determined by the number of input features.
2. Hidden layers: Hidden layers are the layers that come after the input layer and before the output layer. They are called “hidden” because the nodes in these layers are not directly connected to the input or output. The number of hidden layers can vary from problem to problem.
3. Output layer: The output layer is the final layer of the network, and it gives the output. The number of neurons in the output layer is determined by the number of classes that the network is trying to predict.

representation of neural network

Neuron

A neuron is a fundamental unit of a neural network, which is inspired by the structure and function of the biological neurons in the human brain.

In a neural network, a neuron receives input from other neurons or external sources, processes it, and then produces an output, which is transmitted to other neurons. The neuron applies an activation function to the weighted sum of the inputs to produce the output. An Activation Function decides whether a neuron should be activated or not.

To implement neural networks in python

There are several Python libraries that you can use to implement neural networks. Some of the popular ones include:

1. TensorFlow — an open-source library developed by Google that is widely used for deep learning. TensorFlow supports both CPU and GPU computations and provides a high-level API for building neural networks.
2. PyTorch — a popular open-source library that is also widely used for deep learning. It is known for its dynamic computational graph and it has gained popularity in recent years.
3. Keras — a high-level neural network API that can run on top of TensorFlow or Theano. Keras is a great choice for beginners.

Click here to take a look at a simple implementation of a neural network for handwritten digit recognition.

Applications of Neural Networks

Neural networks have various applications in various industries, including healthcare, finance, and technology. Some of the most common applications include:

1. Image and speech recognition
2. Natural language processing
3. Self-driving
4. Stock prediction
5. Medical diagnosis

To conclude, Neural networks are powerful machine learning models that have the ability to learn and improve over time. They are inspired by the structure and function of the human brain. They are used for a wide range of applications, from image and speech recognition to medical diagnosis and financial forecasting. As technology continues to evolve, it’s likely that we’ll see even more applications of neural networks in the future.