In what future tech historians will surely document as humanity’s most elaborate attempt to recreate our own cognitive flaws at scale, Deep Learning has emerged as the technological equivalent of teaching a calculator to have opinions about your Instagram photos. Welcome to the brave new world where we’ve spent billions of dollars building neural networks that can recognize a cat in an image with 99% accuracy but still can’t figure out whether “I’m fine” means you’re actually fine or you’re planning to burn down the office.
Today, dear TechOnion readers, we embark on a journey to demystify Deep Learning, that mystical art of persuading stacks of matrix multiplications to develop something resembling a personality disorder. Prepare for a revelation more shocking than finding out your cloud storage is just someone else’s computer: the “intelligence” in artificial intelligence is about as artificial as the cheese in a vegan pizza.
What Deep Learning Actually Is (When No One’s Trying to Raise Series B Funding)
Strip away the marketing jargon and celestial hype, and deep learning is fundamentally a subset of machine learning that uses artificial neural networks with multiple layers to extract high-level features from raw input.1 In human language: we’re teaching computers to recognize patterns by showing them millions of examples and letting them figure out the commonalities, much like how you taught your grandmother to use Facebook by showing her the same button 47 times.
“Deep learning, a powerful subset of artificial intelligence (AI), is revolutionizing the world around us,” proclaims one suspiciously enthusiastic LinkedIn article. What they don’t mention is that this “revolution” primarily consists of teaching computers to make increasingly confident mistakes at increasingly impressive speeds.
The fundamental architecture resembles a digital nervous system that would make Sigmund Freud reach for stronger cigars: an input layer ingests data, multiple hidden layers transform it with mathematical functions, and an output layer produces a result that’s either eerily accurate or spectacularly wrong.2 There’s no middle ground, which perfectly captures Silicon Valley’s approach to everything.
The Neural Network: Silicon Valley’s Answer to “What If Spreadsheets Had Anxiety?”
At its core, a deep neural network consists of three components: an input layer, hidden layers, and an output layer. The input layer receives data like images, text, or numbers. Each node in this layer passes information to the hidden layers, which apply random parameters to transform the data, similar to how your brain transforms “I should exercise more” into “I deserve ice cream for thinking about exercising.”
These hidden layers are called “hidden” because even the people who designed them aren’t entirely sure what’s happening inside them. It’s the computational equivalent of your teenager’s bedroom – something important is probably happening in there, but you’re too afraid to check.
The transformed data eventually reaches the output layer, which produces a classification, prediction, or generated sample, depending on what you’ve asked the network to do. Through processes called forward propagation and backpropagation, the network gradually adjusts its parameters to reduce errors, much like how humans learn from mistakes, except the neural network doesn’t spend three days in bed questioning its entire existence after getting something wrong.
The Most Honest Definition You’ll Ever Read: “Without all the AI-BS, the only goal of machine learning is to predict results based on incoming data. That’s it,” explains one refreshingly honest machine learning primer. It’s pattern recognition on an industrial scale, like teaching a computer to play “one of these things is not like the other” using millions of examples and the computing power that could have been used to solve climate change.
How to Create Your Very Own Digital Narcissus
Training a deep learning model requires access to a large dataset, which you can find online or collect yourself if you enjoy tedious, soul-crushing labor. Once you have your data, you need to design a neural network that will extract and learn the features of your dataset, a process that one industry insider described as “throwing spaghetti at a wall until something sticks, then pretending you meant to put it there all along.”
For the technically adventurous, platforms like V73 offer pre-built models for tasks like image classification, object detection, and instance segmentation. The process is straightforward:
- Sign up for a free trial (nothing in tech is ever truly free – you’re paying with your soul and data as all TechOnionists know by now)
- Navigate to the “Neural Networks” tab
- Select a model type
- Choose your dataset
- Click “Start Training” and wait while your computer fans scream like they’re auditioning for a death metal band
- Receive an email notification when your model has finished training and is ready to make confident mistakes in production
The alternative is training a model from scratch, which requires the kind of computing resources typically reserved for simulating nuclear explosions or rendering Pixar films. As one deep learning researcher put it during a particularly honest moment at a conference after-party: “My job is basically heating my apartment with GPUs while pretending to understand linear algebra.”
Deep Learning Frameworks: Tribalism for People Who Think They’re Too Smart for Sports
In the world of deep learning, your choice of framework reveals more about your personality than any Myers-Briggs test ever could. The top frameworks in 2025 form an ecosystem more fraught with tribal rivalries than a “Game of Thrones” episode.4
TensorFlow: Google’s offering is the corporate suit of frameworks – powerful, well-resourced, but will absolutely ghost you when you need help with that one obscure error that only occurs every third Tuesday when Jupiter aligns with Mars.
PyTorch: Facebook’s contribution has “gained popularity among researchers and software developers alike” due to its “dynamic computation graph and user-friendly interface”.5 Translation: it’s for people who think they’re too cool for TensorFlow and want everyone at the coffee shop to know it when they loudly complain about “computational graph tracing.”
The Rest of the Pack: The remaining frameworks exist primarily to pad out LinkedIn résumés and give developers something to argue about on X (formerly Twitter.) Choosing the right one is less about technical requirements and more about which tech giants’ Kool-Aid tastes best to you.
The Curious Case of Deep Learning’s Computational Gluttony
The smoking gun evidence of deep learning’s fundamental absurdity is its insatiable hunger for computational resources. “Deep learning is not simple to implement as it requires large amounts of data and substantial computing power. Using a central processing unit (CPU) is rarely enough to train a deep learning net,” admits one analysis.
Connect these seemingly unrelated dots:
- Deep learning requires exponentially more computational power each year
- The same companies building deep learning systems also sell the GPUs required to run them
- Each new state-of-the-art model requires more parameters than the last
The elementary truth becomes clear: deep learning isn’t just a technological breakthrough—it’s the most elaborate planned obsolescence scheme ever devised. By the time you finish reading this article, your cutting-edge neural network will be outdated and require twice the computing power to stay competitive.
Inside the Deep Learning Sweatshop: A Day in the Life
To truly understand the absurdity of deep learning, let’s peek behind the curtain at what deep learning engineers actually do all day.
Meet Aisha Chen, a deep learning engineer at a top-tier AI lab who spends her days doing what she describes as “advanced data janitor work with occasional moments of algorithmic brilliance.”
Her morning routine begins with cleaning data—removing duplicates, handling missing values, and normalizing variables—a process that consumes approximately 80% of her working hours.
“The public thinks I’m building Skynet,” Aisha explains while staring at a spreadsheet with 14 million rows. “The reality is I spent three hours today trying to figure out why our model thinks everyone named ‘null’ is more likely to be a criminal. Turns out someone used the string ‘null’ instead of an actual null value in the database. This is what I got my PhD for.”
By afternoon, Aisha is tuning hyperparameters—the settings that determine how the algorithm learns. “It’s basically just turning knobs until the model performs better,” she sighs. “Sometimes I feel like I’m just playing with a very expensive radio trying to reduce static.”
When asked about the most challenging aspect of her job, Aisha doesn’t hesitate: “Explaining to executives why we need six months and five million dollars to build something that they think should take ‘a couple of days’ because they read an article about how a teenager built a sentiment analyzer for a science fair.”
Deep Learning Applications: Where Dreams Meet Reality
Deep learning has been successfully applied across numerous domains, proving particularly valuable in areas where pattern recognition from large datasets is key.6 Among its most prominent applications:
Computer Vision: Deep learning allows computers to identify objects, people, and activities in images and videos with impressive accuracy. This technology powers everything from self-driving cars to facial recognition systems that definitely won’t be abused by authoritarian regimes in the near future!
Natural Language Processing (NLP): Models like GPT can generate human-like text, answer questions, and even write satirical articles about deep learning that make you question if I’m human. (I am. Probably.)
Healthcare: Deep learning aids in medical image analysis, disease diagnosis, and drug discovery. In one particularly impressive case, a deep learning model discovered a cancer treatment that human researchers had overlooked, then immediately spent three hours trying to convince a patient that they might be interested in purchasing a timeshare in Florida.
Financial Services: From fraud detection to algorithmic trading, deep learning is revolutionizing how money moves, primarily by ensuring it moves from your account to someone else’s faster than ever before.
The Deep Learning Reality Distortion Field
Perhaps the most miraculous aspect of deep learning isn’t the technology itself but the reality distortion field it generates in marketing materials and VC pitches. What researchers describe as “moderately effective pattern matching with significant limitations” becomes “revolutionary AI that will transform humanity” once it passes through a company’s marketing department.
This transformation is evident in how the same technology is described in technical papers versus press releases:
Technical paper: “Our model achieved 73% accuracy in distinguishing between dogs and cats under optimal lighting conditions.”
Press release: “Revolutionary AI breakthrough reimagines visual cognition with superhuman capabilities, disrupting the $14 trillion pet identification market.”
The disconnect extends to how companies talk about computational requirements. Internally, engineers beg for more GPUs while externally, marketing materials boast about “efficient algorithms” that can “run anywhere.” The translation: “Our model requires a data center the size of Luxembourg, but we’ll figure out the mobile version later.”
The Future of Deep Learning: Both More and Less Than We’ve Been Promised
As we look to the future, deep learning stands at a fascinating crossroads. On one path lies the continued refinement of narrow, specialized systems that excel at specific tasks. On the other, more ambitious efforts to create general intelligence that might one day actually understand that when someone says “the restaurant was cold” they’re not just making a factual observation about the ambient temperature.
What’s certain is that deep learning will continue to advance, consuming more data, more computing resources, and more LinkedIn posts about how it’s going to change everything. The algorithms will get smarter in narrow ways while remaining profoundly stupid in others, much like the tech executives funding them.
And as we navigate this future, perhaps the most important question isn’t whether machines can learn deeply but whether we humans can maintain perspective about what they’re actually learning and why. Because at the end of the day, deep learning remains a remarkable tool—capable of incredible pattern recognition while being completely incapable of understanding why recognizing those patterns matters to us in the first place.
After all, as deep learning expert Yoshua Bengio definitely didn’t say during a particularly wine-fueled conference dinner: “We’ve built systems that can recognize a million different objects but can’t understand a single one of them. I’m not sure if that’s genius or just really expensive stupidity.”
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References
- https://www.linkedin.com/pulse/deep-learning-everyone-step-by-step-guide-from-basics-gogul-r-ehsvc ↩︎
- https://www.v7labs.com/blog/deep-learning-guide ↩︎
- https://www.v7labs.com/ ↩︎
- https://365datascience.com/trending/deep-learning-frameworks/ ↩︎
- https://www.harrisonclarke.com/blog/deep-learning-explained-a-thorough-guide-for-data-ai-enthusiasts ↩︎
- https://www.datacamp.com/tutorial/tutorial-deep-learning-tutorial ↩︎
