What Is Deep Learning?
Let’s go back to where it all started: not with a GPU or a neural net, but with a question—and a textbook.
Three years ago, between October and November 2022, a lot happened in the span of two months. Our Tesla finally arrived after months on the waitlist. ChatGPT 3.5 launched. And suddenly, I had one big question: How does all of this actually work?
So I did what any curious, slightly obsessive writer would do—I ordered a copy of Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. If you’ve never cracked it open, it’s roughly the size of a toaster oven and has the same ability to make your brain overheat.
I found a used copy of the textbook so I could highlight the heck out of it. MIT now offers a free digital version. (Link in the resources below.) But I wanted to see the words, feel the pages, and by the end, my fingers seemed to be permanently stained fluorescent yellow. But it was an oddly satisfying deep dive into machine learning. And, I learned so much. The brain of my car began to make sense of me, and “chatbots” took on a whole new dimension.
The math? Let’s just say it made me feel like a guest star on The Big Bang Theory, the kind who nods along and prays no one asks follow-up questions.
But the words—ah, the words! Overfitting. Underfitting. Gradient descent. Stochastic gradient descent. Stochastic parrot. It was like discovering a new dialect of techie elvish, and I was hooked.
Language, Not Equations
I’ve always loved languages. English. German. French. Spanish. Aurebesh. Klingon. ASCII. BASIC. Pascal. Scratch. Python. And I realized, I didn’t need to decode every formula to appreciate what deep learning meant—not just in code, but in culture, in conversation, in how we think about intelligence itself.
So I started sharing a single word a day. That’s it. One concept, one definition, maybe a story. And to my surprise, people started reading. Some were ML PhDs who enjoyed a break from equations. Some were curious newcomers trying to understand what all the hype was about. Some just liked the name.
Eventually, I expanded. AI was suddenly in the news every five minutes. OpenAI. Anthropic. DeepMind. Elon. Apple. The test kitchens of Silicon Valley were cooking up something new every week, and I wanted to taste it all.
But this week, after a few unsubscribes over on the LinkedIn version of this blog, (I dared to mention AI and climate policy in my post last Monday), I realized something: sometimes it’s good to keep things simple. So I’m going back to my roots: one word a day, one concept at a time. Yes, it means I’m switching back to a daily publication. I’ve missed the challenge and the strict discipline of getting a newsletter out every day without fail.
So, let’s begin, shall we? And, of course, we need to start with the obvious.
Deep Learning: The Definition
Deep learning is a subfield of machine learning that uses neural networks with many layers—hence the “deep.” These networks are inspired by the human brain (loosely) and are particularly good at recognizing patterns in data like images, text, and speech.
In simpler terms?
If regular machine learning is like teaching a kid to recognize a cat by pointing out fur, whiskers, and tails, deep learning is like showing the kid a million pictures of cats and letting them figure it out themselves.
It’s why your phone knows your voice, why ChatGPT can write poetry, and why TikTok knows what you want before you do. I’ve never understood TikTok, and it makes my brain hurt, but that’s essentially how deep learning works.
Why It Matters
Deep learning powers:
* Image recognition (think: medical imaging, self-driving cars)
* Speech recognition (Siri, Alexa, Google Assistant)
* Natural language processing (translation, summarization, ChatGPT)
* Recommendation systems (Netflix, Spotify, YouTube)
And it’s just getting started. Deep learning is the engine driving the AI boom.
Tomorrow’s Word
Tomorrow, we will dig into another of the words that now lives in a permanent corner of my brain: stochastic gradient descent. (Spoiler: it’s not nearly as scary as it sounds. And yes, “stochastic” just means “random.”) See? Digging into deep learning is actually rather fun. See you tomorrow.
FAQs
Is deep learning the same as AI? Not exactly. Deep learning is a technique within AI—specifically, within machine learning.
Do you need to understand math to understand deep learning? If you want to be a machine learning engineer, yes. If you want just to understand the concepts, you can certainly do so without doing the calculations. Come along on this journey, and I promise there will be no math involved.
What are neural networks? They’re algorithms modeled (loosely) on the brain’s architecture, made of layers of “neurons” that pass information.
Why is it called ‘deep’? Because of the many layers in the network—each one adds depth.
Can deep learning be dangerous? Like any powerful tool, yes. But understanding it is the first step toward using it responsibly.
Source:
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. Cambridge, MA: MIT Press, 2016.
http://www.deeplearningbook.org
Additional Resources for Inquisitive Minds
Explain deep learning to me in a way that won’t hurt my brain.
Give me that Big Bang Theory Math!
Chapter 1 of Deep Learning video lecture.
Chapter 9 Video Lecture. Convolutional Networks.
Chapter 10. Sequence Modeling.
The Course That Started It All. Start Here!
Want to Learn More? Start with the course that started it all. Andrew Ng’s Deep Learning course on Coursera. (now updated and called: “Deep Learning Specialization.”
This is such a great clip. 14 years ago, this is where Andrew Ng announces he will be offering his machine learning class for free.
Note: Reader @David W Baldwin shared a great story about taking one of the earliest classes with Dr. Ng. See his post below for details on what it was like to be in that amazing course! Maybe you will be inspired to take this iconic course yourself.
Editor’s Note:If you enjoyed this post, please consider sharing it with a friend. I’m committed to keeping all of my blogs and podcasts free for subscribers—no paywalls, no gimmicks. Your shares help me reach more curious minds. Thanks so much for the support.
About the podcast: The podcast attached to this article is an audio overview from Google’s NotebookLM. The sources used in the “notebook” are this article, and the following sources:100 Deep Learning Terms Explained – GeeksforGeeksDeep Learning vs Machine Learning: Key Differences – Syracuse University’s iSchoolDeep Learning: Back to the RootsUnderstanding Supervised Learning: A Guide for Beginners – DEV CommunityWhat Is Deep Learning AI? A Simple Guide With 8 Practical Examples | Bernard MarrWhat Is Deep Learning? | A Beginner’s Guide – ScribbrWhat Is Deep Learning? | IBMWhat is Backpropagation? | IBMWhat is a Neural Network? – Amazon AWSWhat are some of the most impressive Deep Learning websites you’ve encountered? – r/MachineLearning (Reddit)
#artificialintelligence #deeplearning #machinelearning #neuralnetworks #IanGoodfellow #YoshuaBengio #AaronCourville #AndrewNg #DeepLearningtheBook #DeepLearningwiththeWolf #TreeHuggersfortheWin
