Neural networks—a key concept in artificial intelligence (AI) and machine learning. If you’ve been curious about how computers learn and make smart decisions, you’re in the right place!
Before we jump into neural networks, let’s quickly review machine learning. Machine learning is a branch of AI where computers can learn from data and make decisions—without us programming every single rule. It’s super useful for things like spam detection or even self-driving cars. AI and machine learning are everywhere today, and they’re powering more of the tools we use daily.
Let’s use spam email detection as an example. With traditional programming, you’d need to write rules like: if an email contains words like “lottery” or “winner,” mark it as spam. The issue is, spammers constantly change their tricks, so this system would need constant updates.
But with machine learning, we feed the algorithm a lot of emails labeled as spam or not spam, and it learns the patterns on its own. This way, it can adapt to new types of spam without needing us to rewrite rules.
So, what exactly is a neural network?
Neural networks are inspired by how our brains work. They have layers of interconnected nodes or neurons that process data and make predictions. These networks are used in everything from image recognition to natural language processing and even autonomous vehicles.
The structure of a neural network consists of three main layers. First, there’s the input layer, where the raw data comes in. Then, we have the hidden layers, where the network processes and transforms that data. Finally, the output layer gives us the result, like whether an email is spam or not.
Each connection between these neurons has a weight, and there’s also something called a bias. These weights and biases help the neural network learn from the data and make better predictions over time.
One more important piece to understand: activation functions. These functions help the network make complex decisions by introducing non-linearity. A popular one is called ReLU, which helps the network learn efficiently. Another common one, sigmoid, is used when the output needs to be interpreted as a probability, like if we want to know the likelihood that an email is spam.
Neural networks are at the heart of so many AI systems today, from voice assistants like Siri and Alexa to recommendation algorithms on YouTube and Netflix.
I know some of this may seem a bit abstract right now, but don’t worry! In the next articles, we’ll look at different types of neural networks, like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and provide real-life examples to make everything clearer.
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