🧠 Introduction to Deep Learning

Dive into how Deep Learning mimics the human brain using neural networks!

Deep Learning is a subset of Machine Learning that uses layers of algorithms called Artificial Neural Networks. Let's understand it step-by-step, with colorful visuals and simple terms! 🎨

Concept	Meaning	Example
Neurons 🧩	The basic unit of computation (like brain cells) in deep learning models.	Each neuron in an image recognition system detects edges or shapes.
Layers 🎂	Neurons are stacked in layers (input, hidden, output) to learn complex patterns.	Hidden layers allow a model to detect faces from raw pixel data.
Activation Functions ⚡	They decide if a neuron should "fire" or not (introduces non-linearity).	Popular examples: ReLU, Sigmoid, Tanh

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🧩 Neurons

- Receive input signals.
- Perform a weighted sum and apply an activation function.

Think of it like:
🔹 Neuron = Math unit that decides "how much" information passes to the next layer.

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🎂 Layers

- Stack of neurons.
- Input layer (receives raw data), hidden layers (learn features), output layer (gives final prediction).

Example:
🔹 Input: Pixels of a cat 🐱 picture
🔹 Output: Predicts "This is a Cat" ✅

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⚡ Activation Functions

- Introduce non-linearity to the model.
- Allow the network to learn complex relationships.

Popular choices:
🔹 ReLU (Rectified Linear Unit)
🔹 Sigmoid (S-shaped curve)
🔹 Tanh (Hyperbolic tangent)

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🎯 Quick Challenge!

Which function is commonly used to introduce non-linearity in deep learning?

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By Darchums Technologies Inc - April 28, 2025