🧠 Introduction to Neural Networks

Learn the basics of how neural networks function and why they are powerful!

A neural network is a computational model inspired by the way the human brain works. By mimicking neurons, neural networks are able to process data in complex ways. 🧠⚡ Let’s dive into how these networks function! 🚀

Component	Description	Purpose
Neuron	The fundamental unit of a neural network, processing and passing information.	Mimics brain neurons to make decisions.
Weights	Parameters that adjust the strength of connections between neurons.	Determine the importance of inputs to the network.
Activation Function	Decides if a neuron should be activated based on the input it receives.	Adds non-linearity to the model, enabling it to learn complex patterns.

---

🧠 Neuron

- A neuron receives input, applies a weight to it, and then passes it through an activation function.
- The output is sent to the next layer of neurons.

Example:
A neuron could be responsible for determining whether an image contains a cat or not.

---

⚙️ Weights

- Weights are parameters that control the strength of connections between neurons.
- They are adjusted during training to minimize errors in the network’s output.

Example:
If the input to a neuron is the value "5", and the weight is "0.2", the output would be "1" (5 * 0.2).

---

🔑 Activation Function

- The activation function determines if a neuron should be "activated" based on the weighted sum of its inputs.
- It adds non-linearity to the model.

Popular Activation Functions:
🔹 Sigmoid
🔹 ReLU (Rectified Linear Unit)
🔹 Tanh (Hyperbolic Tangent)

---

🎯 Quick Challenge!

What component adjusts the strength of the connection between neurons?

---

By Darchums Technologies Inc - April 28, 2025