mattral / about-neural-networks

Understanding Neural Networks just some beginner-friendly facts; can also see more in my repo "Best-Practices-in-Computer-Vision" for Deep Learning parts

Understanding Neural Networks: A Kid-Friendly Guide

1. Neurons: The Brainy Building Blocks

Think of neural networks as a team of tiny decision-makers called "neurons." Each neuron is like a little robot that looks at information and decides if something is true or not.

2. Activation: Getting Neurons to Talk

Neurons don't just keep their decisions to themselves; they talk to each other. But here's the fun part: these neurons aren't just simple "yes or no" robots. They can be a bit more complicated, and we call this complexity their "activation function."

3. Predictions: Figuring Things Out

Imagine we're trying to predict something, like "Will it rain today?" Neurons work together to make this prediction. Information flows through them, and they practice to get better at making predictions, just like getting better at playing a game.

4. Learning: Getting Smarter

Here's the cool part: if the prediction isn't quite right, we help the neurons learn from their mistakes. We do this by adjusting some settings inside them. This process happens over and over, and it's how neural networks get really good at predicting things.

So, neural networks are like teams of clever robots that work together to make smart guesses and learn from their mistakes. They're like super smart detectives, always improving to make the best predictions!

Repository Guide

Here's a quick overview of what you'll find in our documentation:

1. Introduction

   • A detailed introduction to neural networks.

2. Training Neural Networks

   • Detailed insights into training neural networks, including backpropagation and other essential concepts.

3. Activation Functions

   • Understanding the different activation functions that make neural networks work.

4. Loss Functions

   • Exploring various loss functions used for different machine learning tasks.

5. Backpropagation

   • An in-depth explanation of the backpropagation algorithm used for training neural networks.

6. Overfitting and Regularization

   • Learn how overfitting can be tackled with regularization techniques like dropout and weight decay.

7. Deep Learning and Neural Network Architectures

   • Discover the world of deep learning and key neural network architectures like CNNs, RNNs, and Transformers.

8. Readme.md

   • The one you are reading here.