Machine learning and neural networks are two important technologies in the field of artificial intelligence (AI). While they are often used together, they are not the same thing. In this article, we will explore the differences between machine learning and neural networks and how they are related.

We understood about machine learning in last section so let”s see what neural networks are.

What are Neural Networks?

Neural networks are a type of machine learning algorithm that is inspired by the structure of the human brain. They are designed to simulate the way the brain works by using layers of interconnected nodes, or artificial neurons. Each neuron takes in input from the neurons in the previous layer and uses that input to produce an output. This process is repeated for each layer until a final output is produced.

Neural networks can be used for a wide range of tasks, including image recognition, speech recognition, natural language processing, and prediction. They are particularly well-suited to tasks that involve processing complex data or recognizing patterns in data.

Machine Learning vs. Neural Networks

Now that we have a basic understanding of what machine learning and neural networks are, let”s dive deeper into the differences between the two.

• Firstly, machine learning is a broad category that encompasses many different types of algorithms, including neural networks. Neural networks are a specific type of machine learning algorithm that is designed to simulate the way the brain works.

• Secondly, while machine learning algorithms can be used for a wide range of tasks, neural networks are particularly well-suited to tasks that involve processing complex data or recognizing patterns in data. Neural networks can recognize complex patterns and relationships in data that other machine learning algorithms may not be able to detect.

• Thirdly, neural networks require a lot of data and processing power to train. Neural networks typically require large datasets and powerful hardware, such as graphics processing units (GPUs), to train effectively. Machine learning algorithms, on the other hand, can be trained on smaller datasets and less powerful hardware.

• Finally, neural networks can provide highly accurate predictions and decisions, but they can be more difficult to understand and interpret than other machine learning algorithms. The way that neural networks make decisions is not always transparent, which can make it difficult to understand how they arrived at their conclusions.