Neural Network

Input Layer

Imagine you have a basket full of different fruits (like apples, bananas, oranges) and you want your robot friend to sort them out. You show the fruits to the robot one by one. This showing part is like the 'input layer' in a neural network - it's where the robot (or neural network) first sees what you give it.

Hidden Layers

Once the robot sees the fruit, it needs to think and figure out what fruit it is. The robot's thinking process is like the 'hidden layers' in a neural network. It's where all the learning and understanding happens.

Output Layer

After thinking, the robot makes its decision and tells you what fruit it thinks you gave it. This final decision-making step is like the 'output layer' in a neural network. It's where the network gives us the answer after all the thinking and processing.

Neurons (or Nodes)

Think of the neurons as little robot helpers inside the robot's brain (hidden layers) that do the work. They look at the fruit, think about it, pass messages along to each other, and help the robot make its decision.

Weights and Biases

Each of these robot helpers (neurons) have their own little rules to help them think about what fruit you gave them. 'Weights' and 'biases' are like these rules. They help each neuron decide how important the information it's looking at is.

Activation Function

It's like a game rule the little robot helpers use to decide whether to pass a message along or not. If the game rule says "yes", the message gets passed along. If the game rule says "no", the message is stopped.

Loss Function

This is like a scoring system that tells the robot how well or how badly it did on guessing the fruit. If the robot makes a good guess, it gets a low score (which is good!). If it makes a bad guess, it gets a high score. The robot's goal is to get the lowest score it can, which means it's making good guesses!

Channel

Imagine the fruits in the basket are not just apples, oranges, and bananas, but also painted in different colors like red, blue, and green. The robot (our neural network) needs to look at both the shape of the fruit and the color to sort them. Each color can be thought of as a separate 'channel'. The robot first looks at all the red parts of the fruits, then the blue parts, then the green parts. In image recognition tasks in AI, these channels typically refer to the color channels of an image - Red, Green, and Blue.

In depth reference can be found here

Kernel

The kernel is like a small magnifying glass the robot uses to look closely at each fruit. It allows the robot to notice specific details, like a spot or a specific pattern on the skin of the fruit. In the context of neural networks, a kernel is a small matrix used for features like edge detection, blur, and sharpen that help the model to understand images better.

In depth reference can be found here

Receptive Field

This term refers to the part of the image that a particular neuron 'sees' or is 'focused on'. It's like if the robot was looking through a small window or hole at only a part of each fruit at a time, rather than the whole fruit. This 'window' is the receptive field.

In depth reference can be found here