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Neural networks

A neural network is a type of machine learning tool that is inspired by the way that our brain works. It consists of many small parts called “neurons” that work together to solve a particular problem. Neural networks can be used in supervised learning, unsupervised learning, and reinforcement learning.

How do they work?

To understand how a neural network operates, think of it like a team of detectives trying to solve a mystery: Each detective brings a different set of skills and knowledge to the investigation; they all work together to piece together clues and ultimately, solve the case.

Similarly, in a neural network, each neuron is responsible for analyzing a specific aspect of the data. It then passes its findings to the other neurons. By working together, they can make increasingly accurate predictions or classifications.

For example, say there’s a neural network designed for recognizing handwritten numbers. The first layer of neurons might look at the individual pixels in the image, and then try to identify simple features like lines or curves. 

The next layer might combine these features to recognize more complex patterns, such as loops or hooks. And then the final layer would combine all of this information to make a prediction about which number or digit is being shown. It’s pretty neat how they come together to solve a problem! 

There are different types of neural networks, each with their own special purpose. Some are good at recognizing pictures, while others are good at understanding words. The thing to remember is that they are flexible, adaptable, and can handle different types of information. 

Continue the Convo: Imagine you are part of a team developing a self-driving car. How could neural networks be used in the car's decision-making process? What factors are crucial when navigating through traffic and making driving decisions? Additionally, what are some potential advantages and challenges of using neural networks in autonomous vehicles?

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