13. Deep Learning and Neural Networks

What is Deep Learning?

Deep Learning is a subset of Machine Learning focused on algorithms inspired by the structure and function of the brain called Artificial Neural Networks. It involves training computers to learn from large amounts of data, allowing them to make decisions, recognize patterns, and solve complex problems.

What is a Neural Network?

A Neural Network is a system of algorithms that attempts to recognize relationships in data through a process that mimics the way the human brain operates.

Basic Structure of a Neural Network:

Input Layer – Receives the input data (e.g., an image or sentence).

Hidden Layers – Intermediate layers where the actual processing happens through mathematical computations.

Output Layer – Produces the final result (e.g., a class label or predicted value).

Each layer contains neurons (also called nodes), and each connection between neurons has a weight that adjusts as learning progresses.

How Does Deep Learning Work?

Deep learning models learn by adjusting the weights of the neural network based on the error between the predicted output and the actual result. This process is called training, and it uses an algorithm called backpropagation along with an optimizer like Stochastic Gradient Descent (SGD) or Adam.

Key Concepts in Deep Learning:

Activation Functions: Decide whether a neuron should be activated. Common ones include ReLU, Sigmoid, and Tanh.

Loss Function: Measures how wrong the model’s predictions are. Examples: Mean Squared Error, Cross-Entropy Loss.

Epochs: One full pass through the training dataset.

Overfitting/Underfitting: Overfitting means the model is too closely fit to the training data; underfitting means the model is too simple to learn the patterns.

Types of Neural Networks:

Feedforward Neural Networks (FNNs) – The simplest type, where information moves in one direction.

Convolutional Neural Networks (CNNs) – Great for image data (e.g., face recognition, object detection).

Recurrent Neural Networks (RNNs) – Useful for sequential data (e.g., text, time series).

Transformers – Advanced models that have revolutionized natural language processing (e.g., used in ChatGPT).

Applications of Deep Learning:

Image and speech recognition

Natural language processing

Autonomous vehicles

Medical diagnostics

Recommendation systems

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