Deep Learning and Neural Networks

Deep Learning and Neural Networks

Machine Learning lets computers learn from data, but some tasks are very complex, like recognizing faces in photos or understanding speech. To handle these tasks, AI designers use a method called deep learning.

Deep learning uses neural networks, which are made of layers that process information step by step. Each layer builds on the one before it, combining simple patterns into more complex ones. You can think of this like abstraction in computer science, where each layer handles a small piece of a problem and passes its result along.

This layered approach helps AI do things that would be very difficult with regular step-by-step programming. However, neural networks often act like a “black box”. We can see what goes in and what comes out, but it can be hard to know exactly how the AI made its decision. This is why AI can sometimes make mistakes or show unexpected biases.

Machine Learning and Computer Vision

Computer Vision is a field of AI that allows computers to “see” and understand images or videos. It combines Machine Learning and Deep Learning to recognize patterns in visual data.

For example, AI can:

• Identify objects in photos, like cars, animals, or plants

• Detect faces for security or authentication

• Recognize handwritten text or numbers

• Track motion in videos