Introduction to Deep Learning

Welcome to Introduction to Deep Learning. This module is made for anyone interested in AI and Deep Learning as an introductory material.

What will be covered :

-Deep Learning and Artificial Neural Networks

-How do Machines Learn and what is the role of Data Science

-Basic principles of Deep Learning

-Resources

-How to get started

Note (the material is composed of multiple topics and you can move to the next topic by pressing the 'Next' button' at the bottom of each page)

Deep Learning for AI and Neural Networks

Deep Learning has emerged as a dominant technology for the development artificial intelligence (AI) today. While the history of Deep Learning traces back to 1940s and the works of Walter Pitts and Warren McCulloch to try to create first neural models resembling how human Neurons work, Deep Learning has achieved its full potential in the last decade. Its potential is supercharged mostly by its use in the AI field. In fact, most AI models today are created using the Deep Learning approaches. Deep learning employs Artificial Neural Networks (ANNs) able to learn and interpret complex data patterns by Machines.

This introduction explores the foundational principles of Deep Learning and ANNs, the art approaches to learn intricate and complex patterns in the data, their inspiration from the Human Brain, and their extensive applications in today's AI-driven world.

Deep learning draws significant portion of how it works from the human brain's architecture and functioning. Just as neurons in the brain communicate through synapses by sending electric signals, artificial neurons in a neural network also transmit mathematical signals via weighted connections. The learning process in Artificial Neural Networks mirrors how the brain strengthens synaptic connections through experience (Data) and repetition (Training iterations in Deep Learning). During Training, Neural Networks adjust their mathematical weights based on the error of their predictions, to learn, similarly how brain adjusts synaptic strengths to enhance cognitive functions. This biological analogy provides a foundational framework for developing models capable of sophisticated pattern recognition and decision-making in Deep Learning and AI. To understand more about how Artificial Neural Networks are designed, click the NEXT button.