====== Introduction ======

==== The Dream of Thinking Machines ====
Humanity has dreamed of creating machines that think for a very long time, a desire that stretches back as far as ancient Greece. Long before the first computer was even built, visionaries like Ada Lovelace wondered if programmable machines might one day possess intelligence. Today, that dream is a thriving reality known as artificial intelligence (AI), used to automate labor, diagnose diseases, and understand speech.

==== The Challenge of Intuition ====
In the early days, AI easily solved problems that humans find intellectually difficult, such as playing Chess, because these tasks follow formal mathematical rules. However, the true challenge was solving tasks that humans find "easy" and automatic, like recognizing a face or understanding spoken words. These tasks are difficult for computers because they are intuitive; we cannot easily describe the formal rules for how a face looks in terms of pixel values.

==== The Solution: Deep Learning ====
In their seminal book **//Deep Learning//**, authors **Ian Goodfellow**, **Yoshua Bengio**, and **Aaron Courville** present a solution to these intuitive problems:
  * **Learning from Experience**: Rather than relying on human operators to manually code strict rules, deep learning allows computers to gather knowledge directly from experience.
  * **Hierarchy of Concepts**: The core idea is to let the computer understand the world through a hierarchy. It learns complicated concepts by building them out of simpler ones.
  * **Why "Deep"?**: If you draw a graph showing how these concepts are built on top of one another, the graph has many layers. Because of this depth, we call the approach "deep learning".

For example, a deep learning system might start by recognizing simple edges in an image, combine those edges to identify corners and contours, use those to find specific object parts, and finally recognize the identity of the object, such as a person or a car.

==== A History of Three Waves ====
Goodfellow, Bengio, and Courville note that while deep learning feels new, it actually has a rich history dating back to the 1940s. The field has evolved through three main historical waves:
  - **Cybernetics (1940s–1960s)**: The earliest phase, which focused on biologically inspired learning.
  - **Connectionism (1980s–1990s)**: A movement that theorized that simple computational units could achieve intelligent behavior when networked together.
  - **Deep Learning (2006–Present)**: The current resurgence, driven by the ability to train deeper networks than ever before.

==== Why Now? ====
You might wonder why this technology is taking off now if the ideas are decades old. The authors explain that the explosion of deep learning today is driven by two massive changes:
  * **Big Data**: The digitization of society has provided the massive datasets necessary to train these systems. We now have millions of labeled examples that allow algorithms to match or exceed human performance.
  * **Computational Power**: We finally have the hardware to run large models. Faster computers and technologies like GPUs allow us to build neural networks that are much larger and more powerful than those in the 1980s.

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**Source:**
Based on Chapter 1 of //Deep Learning// by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.