I’ve been getting into data science and machine learning lately. This blog post serves as a public notebook and knowledge check. This subject is also just really cool.

## The human neural network

The human brain has around 86 billion neurons.

Neurons are basic building blocks used to exchange info with other neurons via ⚡ electrical pulses.

One neuron doesn’t talk directly with all 80 billion others. Rather, they are connected into structures that perform specialized functions. These structures are biological neural networks.

Brains learn from experience. When you learn something new, neurons in your brain 💪 strengthen their lines of connection with some neurons and prune their connection to others.

## Artificial neural networks

Humans and computers share many similarities and differences.

Humans perceive and interpret everything based on their previous experiences. We make decisions using reasoning and consider all of our life experiences.

On the other hand, computers lack the ability to reason or understand the meaning of data in the same way humans do. They can only follow instructions.

However, computers can learn through training. Artificial neural networks learn through trial and error (or failing a lot).

During training, the network will make a lot of mistakes. These errors are then used to ⚖️ adjust the connections between neurons so that the network can be more correct in the future. See how this process is similar to how humans learn?

Continue this process hundreds or thousands of times, and you’ll end up with a somewhat smart neural network, capable of making complex decisions!