Deep Learning : The Introduction

In this episode, we open Season 3 of the Adapticx Podcast by stepping into one of the most significant shifts in the history of artificial intelligence: deep learning. After building a strong foundation in Season 2—how machines learn from data, how classical algorithms work, and what it takes to evaluate and deploy ML systems—we now move to the models that transformed the entire field.This season begins with a simple but revolutionary question: what happens when we stack many layers of connected units and let them learn representations of the world on their own? That idea became the engine behind modern AI, and in this introduction, we set the stage for exploring it clearly and conversationally, without jargon or unnecessary math.We look at why deep learning succeeded after decades of stalled progress, how changes in compute, data, and algorithms ignited its rise, and what makes multilayer networks capable of learning powerful features automatically. We also preview the key architectures and engineering tools that shaped the evolution of deep learning—from CNNs and RNNs to autoencoders, GANs, GPUs, and distributed training—and how these advances eventually led to today’s large-scale foundation models.This episode covers:• Why deep learning re-emerged and became the dominant paradigm in AI • How neurons, layers, and backpropagation form the foundation of modern models • The architectures that defined eras of progress: CNNs, RNNs, autoencoders, GANs • The practical engineering behind large-scale deep learning systems • How these ideas led to foundation models and the AI landscape we now live inThis episode is part of the Adapticx AI Podcast. You can listen using the link provided, or by searching “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.https://adapticx.co.uk