🧠 Introduction to Neural Networks and Deep Learning

📘 What is a Neural Network?

A neural network is a computer system inspired by the structure and function of the human brain. It’s a fundamental concept in machine learning and artificial intelligence (AI).

At its core, a neural network is made up of layers of interconnected nodes (also called neurons). Each node takes input, processes it, and passes it to the next layer.

📊 Basic Structure of a Neural Network

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Input Layer → Hidden Layers → Output Layer

1. Input Layer

Takes raw data (e.g., pixel values, text, numbers).

2. Hidden Layers

These layers do the computation through weights, biases, and activation functions.

The more hidden layers, the "deeper" the network — which is where deep learning comes in.

3. Output Layer

Produces the final result (e.g., class label, predicted value).

⚙️ How Does a Neural Network Learn?

The network learns by adjusting weights through a process called training, typically using a method called backpropagation.

Training involves:

Forward Pass – Compute predictions.

Loss Function – Measure the error.

Backward Pass – Adjust weights to reduce error using an optimization algorithm like gradient descent.

🔍 What is Deep Learning?

Deep Learning is a subfield of machine learning that uses deep neural networks (with many hidden layers) to learn patterns in data.

It is particularly effective for tasks like:

Image recognition

Natural language processing (NLP)

Speech recognition

Recommendation systems

Autonomous vehicles

🧱 Types of Neural Networks

Type Description Use Case

Feedforward Neural Network (FNN) Basic structure where data moves in one direction Classification, Regression

Convolutional Neural Network (CNN) Designed to process images using filters Image and video recognition

Recurrent Neural Network (RNN) Handles sequential data with memory Text, speech, time series

Transformers Replaces RNNs in NLP tasks ChatGPT, language translation

🔢 Activation Functions

These introduce non-linearity into the model:

ReLU (Rectified Linear Unit): Most common

Sigmoid: Outputs between 0 and 1

Tanh: Outputs between -1 and 1

Softmax: Used in classification to get probabilities

🧪 Applications of Neural Networks

📸 Image Classification: Recognize objects in photos

🗣️ Speech-to-Text: Convert spoken language into text

🧾 Text Generation: GPT models generate human-like text

🚗 Self-driving Cars: Interpret sensor input

💬 Chatbots: Understand and respond to text inputs

🛠️ Popular Deep Learning Frameworks

Framework Language Description

TensorFlow Python Developed by Google, widely used

PyTorch Python Developed by Facebook, popular in research

Keras Python High-level API for TensorFlow

ONNX Cross-platform Open format to share models

📈 Summary

Term Meaning

Neural Network A series of layers that mimic brain neurons

Deep Learning Using neural networks with many layers

Training Process of learning by adjusting weights

Backpropagation How the network learns from errors

Activation Function Adds non-linearity to the network

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