Demystifying Deep Learning
Part 1 of 2: A First Principles Guide to Neural Network Prediction, Learning, and Core Implementation
Important - This is a lengthy article—the first of a two-part series—where we explore every fundamental aspect of deep learning in depth. Consider saving or bookmarking it, as it will take time to read through. Think of it as having a comprehensive book on deep learning delivered straight to your inbox.
Deep learning, an exciting intersection of machine learning and artificial intelligence, is causing a major disruption in society and industry. It's one of the most valuable skills you can learn in this century. I'm thrilled you're here, and I hope you'll share my excitement as we dive into this powerful and pervasive field.
Deep learning methods are changing the world. Its applications are everywhere, from optimizing car engines to determining what you see on social media. This technology is not just powerful; it's fun!
Here, we'll explore the core concepts:
Deep Learning: A specialized area within machine learning and artificial intelligence, driving significant advancements and changes in various fields.
Machine Learning: A broader field that encompasses deep learning, focusing on algorithms that allow computers to learn from data.
Artificial Intelligence: The overarching field encompassing both machine learning and deep learning, aiming to create intelligent systems.
By the end of this journey, you will have gained highly valuable and impactful skills applicable across diverse domains.
Why you should learn deep learning
This section explores three compelling reasons to learn deep learning: its power to automate intelligence, its potential impact on skilled labor (including job displacement), and its inherent fun and creative aspects.
Deep Learning as an Automation Tool
Deep learning represents the latest chapter in humanity's ongoing quest to build tools for understanding and controlling our environment. Unlike previous innovations, deep learning focuses on automating intelligence itself. This "mental innovation" builds upon earlier advances in AI and machine learning, incrementally automating increasingly complex cognitive tasks. Deep learning has already achieved remarkable success in diverse fields like computer vision, speech recognition, and machine translation, primarily using a single, brain-inspired algorithm: neural networks. The versatility of this approach is striking, and recent advancements suggest that deep learning might even offer a window into the workings of our own minds.
Impact on Skilled Labor and Job Displacement
While often overhyped, the potential for deep learning to automate skilled labor is a significant implication that deserves serious consideration. Even with current capabilities, deep learning can profoundly impact skilled labor globally. Professions like call-center operators, taxi drivers, and low-level business analysts are particularly vulnerable, as deep learning offers a low-cost alternative in these areas. This potential for job displacement underscores the importance of adapting to the changing landscape and considering a transition into the growing deep learning industry.
Deep Learning as a Fun and Creative Pursuit
Beyond its practical applications and economic implications, learning deep learning is a fun and inspiring endeavor. It offers a unique intersection between human and machine intelligence, providing a fascinating opportunity to explore what it means to think, reason, and create. Simulating intelligence and creativity in machines can be both enlightening and inspiring. Imagine teaching a machine to paint like Monet—it's mind-bogglingly cool! This creative aspect of deep learning adds another layer of excitement and intellectual reward to the learning process.
Key Terms
Incremental Automation of Intelligence: The process by which deep learning gradually automates cognitive tasks, building upon previous successes in AI and machine learning.
Mental Innovation: A type of innovation, exemplified by deep learning, that focuses on automating intellectual or cognitive processes, as opposed to purely mechanical ones.
Neural Networks: The brain-inspired algorithm largely used by deep learning to achieve its accomplishments across various fields.
Job Displacement: The phenomenon where automation, particularly through deep learning, replaces human jobs, especially in skilled labor sectors.
Will this be difficult to learn?
This section addresses the often-asked question about the difficulty of learning deep learning. The focus here is on the "fun payoff"—seeing something you've built actually learning. You'll experience this very early on, by Chapter 3, and these payoffs are structured throughout the book to keep you engaged and motivated.
The Fun Payoff
A core concept in this book is the "fun payoff." This is the rewarding experience of witnessing something you've created actually learn and adapt. This early and tangible result helps address concerns about the perceived difficulty of deep learning, providing immediate gratification and motivation to continue learning.
Managing Expectations and Maintaining Engagement
This book is designed to provide a smooth learning curve with frequent "fun payoffs." Instead of a long slog through abstract theory before any practical application, you'll quickly build a working neural network, giving you a sense of accomplishment and fueling your desire to learn more.