What is Artificial Neural Network?

Artificial Neural Network

1st prompt I throwed to GenAI to lower the learning curve:

Can you explain ANN so that elementary students can understand while you don’t omit or miss any important concept?

2nd prompt to learn ANN in more detail:

Okay now, can you explain this in undergraduate level?

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Answer for prompt 1 starts here:

Artificial Neural Networks for Kids! 🧠

Imagine your brain. It’s super powerful, right? It lets you learn, remember, and recognize your dog in a park full of other dogs. An Artificial Neural Network is like a computer program that tries to copy how your brain works, but in a much simpler way.

Let’s break it down!

1. Meet a Neuron: The Tiny Helper

Your brain is made of billions of tiny cells called neurons. They are like tiny helpers that pass messages to each other. An artificial neuron (we’ll just call it a neuron) does the same thing!

A single neuron is pretty simple. It just does three things:

• Receives information: Other neurons pass it little bits of information.
• Makes a simple decision: It looks at all the info it received and follows a very simple rule, like, “If the total excitement level is more than 5, I will get excited too!”
• Passes a message: If it gets excited, it sends its own “I’m excited!” message to the next group of neurons.

One neuron by itself isn’t very smart. But when you put a lot of them on a team, they can do amazing things!

2. Putting the Helpers on a Team: The Network

A neural network is just a big team of these neurons organized into layers. Think of it like a game of telephone, but for solving problems.

There are three types of layers:

• The Input Layer: The Lookouts 👀 This is the first layer. Its only job is to see the problem. If you show the network a picture of a cat, the input neurons are the lookouts. One lookout might shout, “I see a pointy ear!” Another might say, “I see a long whisker!” They don’t think; they just report the basic facts.

• The Hidden Layers: The Detectives 🕵️ These are the middle layers, and this is where the real thinking happens. The detectives in the first hidden layer listen to the lookouts. They might combine the messages: “Pointy ear + whisker… hmm, that sounds like part of an animal’s face.” The next layer of detectives listens to them and combines their clues into even smarter ideas, like, “Animal face + fur + small nose… this is probably a cat or a small dog.” The more hidden layers you have, the more complex ideas the network can form.

• The Output Layer: The Final Boss 🏆 This is the last layer. It has one or a few neurons that make the final decision. It listens to all the clues from the last detective layer and makes the final call. For example, it might say, “I am 98% sure this is a CAT!”

3. How the Team Learns: Training the Network 🏋️‍♂️

So how does the network get smart enough to tell a cat from a dog? You have to train it. It’s like studying for a test with thousands of flashcards.

Here’s how it works:

1. Show and Guess: You show the network a picture (e.g., a cat) and say, “What is this?” The network, which is dumb at first, makes a random guess, maybe “It’s a car!”
2. Check the Answer: You tell the network, “Nope! That was a cat. Your guess was very wrong.” The difference between the wrong guess (“car”) and the right answer (“cat”) is called the error.
3. Learn from the Mistake: This is the most important part! A special algorithm goes backward through the network (from the final boss to the lookouts) and tells each neuron how much it contributed to the mistake.
4. Adjust the Connections (This is the secret!): The connections between neurons have something called a weight. You can think of a weight as an importance knob.
   • If one neuron gave a really bad tip that led to the wrong answer, the network turns down the importance knob on its connection. It’s like saying, “I’m not going to listen to you as much next time.”
   • If another neuron gave a helpful tip, the network turns up the importance knob for it. “Your advice was good! I’ll listen to you more!”

By showing the network thousands (or millions!) of pictures of cats, dogs, birds, and cars, and adjusting these “importance knobs” every single time it makes a mistake, the network slowly gets better and better. Eventually, it becomes an expert at recognizing what’s in a picture.

And that’s it! A neural network is just a team of simple helpers that, when working together and learning from their mistakes, can solve incredibly complex problems.

Answer for prompt 2 starts here:

Artificial Neural Networks: A University-Level Overview

An Artificial Neural Network (ANN) is a computational model inspired by the structure of the human brain. It’s a foundational component of modern machine learning, designed to recognize complex patterns in data.

1. The Core Unit: The Neuron

A neuron is the fundamental processing unit of the network. It transforms inputs into an output.

• Inputs (\(x_i\)): A set of numerical values, either from the raw dataset or the outputs of a previous neuron layer.
• Weights (\(w_i\)): A parameter for each input, signifying its importance. These are what the network learns during training.
• Bias (\(b\)): A learnable constant that allows shifting the activation function, increasing model flexibility.
• Activation Function (\(f\)): A non-linear function applied to the weighted sum of inputs plus the bias. This is crucial; without non-linearity, a deep network would behave like a single, simple linear model. Common examples include ReLU, Sigmoid, and Tanh.

The neuron’s output is calculated as: \(y = f\left(\sum_{i=1}^{n} (w_i x_i) + b\right)\)

2. The Architecture: Layers of Neurons

Neurons are organized into layers. Data flows from the input layer to the output layer.

• Input Layer: Receives the raw data (features). The number of neurons corresponds to the number of features in the dataset.
• Hidden Layers: One or more layers between the input and output. They are responsible for learning and extracting progressively more complex features from the data. The “deep” in “deep learning” refers to having multiple hidden layers.
• Output Layer: Produces the final prediction. Its structure depends on the task:
  • Regression: Typically a single neuron with no activation function, outputting a continuous value.
  • Classification: Often one neuron per class with a Softmax activation function to output a probability distribution across the classes.

3. The Learning Process: Training the Network

The network “learns” by iteratively adjusting its weights and biases to minimize prediction error. This process is called training.

1. Forward Propagation: Input data is fed through the network. Each neuron calculates its output and passes it to the next layer until a final prediction is made by the output layer.

2. Loss Calculation: A loss function (or cost function) measures the error between the network’s prediction (\(\hat{y}\)) and the true target (\(y\)).
   • Mean Squared Error (MSE) is common for regression.
   • Cross-Entropy Loss is common for classification.

3. Backpropagation: The core of learning. This algorithm efficiently calculates the gradient of the loss function with respect to every weight and bias in the network. It works by propagating the error backward from the output layer to the input layer, using the chain rule from calculus to determine how much each parameter contributed to the total error.

4. Weight Update: An optimization algorithm, most commonly Gradient Descent (or its variants like Adam), uses these gradients to update the weights and biases. The parameters are adjusted in the direction that most steeply decreases the loss. The size of this adjustment is controlled by the learning rate.

This cycle of forward propagation -> loss calculation -> backpropagation -> weight update is repeated for many epochs (passes through the entire dataset) until the model’s performance converges.