Hey, Welcome to Automation ES!
So you landed here. Good.
Look, here’s what’s actually going on. We started Automation ES because the tech world moves crazy fast, and most people are getting left behind. Deep learning, AI, automation—these things are changing everything, but the explanations out there are either too complicated or just flat-out wrong. We needed a place where people could actually understand this stuff. Real explanations. Real use cases. Not marketing hype.
Our readers? They’re trying to keep up. They want to understand how deep learning actually works. They’re curious about AI but don’t want it dumbed down to the point of being useless. They want to know what’s actually possible right now versus what’s still science fiction. They care about practical applications and real results, not just buzzwords.
If you know deep learning, if you’ve actually built models or worked with AI, and you want to reach people who will actually get what you’re saying—we want you. Whether you’re a machine learning engineer, a data scientist, someone with a computer science degree, or you’ve just spent years learning this on your own, there’s probably space for you here.
This is about real knowledge. Not getting clicks, not selling courses, and not actually helping people understand one of the most critical technologies happening right now.
Who Should Write for Us?
Alright, let me be straight. We’re picky about who writes here, but like, in a good way. We need people who actually know what they’re talking about.
If you fit any of these, we’re interested:
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Machine learning engineers – People who actually build and deploy models
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Data scientists with real experience – You work with data and AI regularly
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Computer science academics or researchers – You understand the theory and can explain it
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AI specialists and neural network experts – You know deep learning inside and out
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Software engineers who work with AI – You’ve integrated AI into actual products
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People with ML certifications – Andrew Ng’s stuff, fast.ai, whatever—you studied this seriously
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Tech bloggers or educators – You know how to explain complex things to humans
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Anyone who’s genuinely studied deep learning – And can back it up with projects or experience, not just hype
Here’s the real deal, though—you don’t need a PhD from MIT. Some of the best AI knowledge comes from people who’ve just built, broken, and fixed it, and learned what actually works. What you DO need is actual knowledge. Real understanding. We can tell when someone’s just repeating marketing speak, and so can our readers.
Topics We’re Actually Hungry For
So like, I could go on forever, but here’s what we’re genuinely looking for right now:
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Deep learning fundamentals explained – Neurons, layers, backpropagation, all that stuff
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Neural network architectures – CNNs, RNNs, Transformers, how they’re different, when to use them
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Natural language processing (NLP) with deep learning – ChatGPT, language models, how they work
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Computer vision and image recognition – Real applications, not just theory
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Getting started with deep learning – TensorFlow, PyTorch, Keras—practical guides for beginners
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Data preprocessing for deep learning – Because this is like 80% of the work, and nobody talks about it
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Transfer learning and pre-trained models – How to actually use existing models instead of building from scratch
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Deep learning for time series and forecasting – Stock prices, weather, whatever
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Generative AI and deep learning – How models actually generate text, images, etc
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AI ethics and bias in deep learning – The real problems nobody wants to talk about
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GPU optimization and training efficiency – Making models run faster without spending your whole salary
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Deep learning in production – Not just notebooks, actual deployment and monitoring
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Automation using AI and deep learning – How businesses are actually using this stuff
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Emerging AI tech and what’s actually coming next – Spoiler: some of it’s hype
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Deep learning for healthcare, finance, or other industries – Real-world applications
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Common mistakes people make with deep learning – Learning from failures
If you have an angle that helps people understand or use deep learning, pitch it. We’re not going to be weird about it.
What We Actually Need From Your Article
O,K so here’s what makes an article work for Automation ES:
How long: Aim for somewhere between 1,500 and 2,500 words. Quick how-to stuff or tutorials? 800–1,200 is cool. But don’t pad it with garbage just to hit a number. That’s la,zy, and readers can tell.
Sound like a real person: Write like you’re explaining this to someone bright but new to deep learning. Short sentences. Paragraphs that don’t go on forever. No unnecessary jargon. If you must use a technical term, explain it. Most people aren’t getting PhDs in this.
Make it yours: Your article must be original. Not copying from GitHub READMEs, not rewording TensorFlow documentation, and not stealing from other tech blogs. We want what’s in your brain.
Show proof: If you’re claiming how something works, back it up. Code examples? Great. Link to research? Perfect. Be honest about where your information comes from. Our readers are intelligent, and they appreciate transparency.
Make it readable: Use headings so people can scan it. Start with something interesting that captures their attention. End with tangible takeaways they can actually use. Don’t bury the important stuff.
Keywords should feel natural: Your main topic (deep learning or whatever angle you’re taking) should show up naturally in the intro, in a heading or two, and at the end. But like, don’t force it. If it sounds weird, it sounds funny.
Link to our other stuff: When it makes sense, link to other Automation ES articles. Talking about neural networks? Maybe reference another deep learning piece. Discussing AI ethics? Link to something relevant. Helps readers find more high-quality content and, honestly, helps us too.
Give us your SEO title and description: Tell us what you’d call it in Google (under 60 characters) and write something short that would make someone actually click (under 155 characters).
How to Format Your Article
I know formatting sounds like no fun, but it actually matters when people are reading on their phones, at work, or late at n,ight learning this stuff:
Headings: Use H2 and H3. That’s it. Stop there. People need clear markers to know which section they’re in.
Keep paragraphs short: 2–4 sentences max. Long blocks of text kill the reading experience, especially on mobile. People’s attention is already stretched thin.
Use lists: Bullets for benefits, tips, or lists of items—numbers for step-by-step instructions. People love lists. Easy to scan.
Bold the stuff that matters: If there’s something significant, dare it. Just don’t overdo it, or it loses impact.
Use code examples: If you’re explaining a coding concept, show code. Make it clean. Explain what it does. People learn better with actual examples.
Include images or diagrams: Got screenshots, graphs, or infographics? Send them. Make sure you own them or have permission to use them. Royalty-free is fine.
Real examples help: Talk about actual projects or use cases. Show how this works in practice.
The Link Rules (Keeping It Real)
Here’s the deal with links:
Our articles: YES. Link to other Automation ES posts when relevant. Helps readers find more content and helps us.
Legit tech and AI resources: YES. GitHub repos, research papers, TensorFlow docs, official frameworks, that kind of thing.
Your own products or courses: NO. Don’t link to your AI course or your startup. We know what you’re doing.
Affiliate links: NO. No commissions hidden in helpful advice. People see through it.
Spam or random promotional stuff: NO. Links to some random AI platform you don’t actually use? Nope.
Your website in your bio: YES. One link. Keep it professional.
Simple rule: Does this link actually help the person reading? Include it. Is it just promotional? Don’t do it.
Write Us Your Author Bio
At the end of your article, include a short author bio about yourself. Keep it honest. 50–100 words. Tell us:
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Your name and what you actually do in tech/AI
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Any education or credentials
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What you specialize in or are known for
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can peoplele can find you
Here’s an example:
Alex is a machine learning engineer with 7 years of experience building production AI systems. They’ve worked with deep learning at scale for e-commerce and financial services companies. They’re focused on making complex concepts understandable and love contributing to open source. Find them on GitHub or LinkedIn.
How to Actually Submit Your Article
You wrote something solid. Now what?
Step 1: Email us your pitch first. Don’t send the whole article yet. Subject line: “Guest Article Pitch: [Your Headline Here].”
Tell us what the article’s about (a few bullet points), why our readers would care, and why you’re qualified to write it. Keep it short—a paragraph max.
Step 2: Wait for our response. Usually takes like 5–7 business days. If we like it, we say yes. If it’s not right, we’ll be honest.
Step 3: Write the full article. Follow what I mentioned above. Make it good. It’s honest. Make it actually helpful.
Step 4: Send it as a Google Doc or Word file. Include your SEO title, meta description, author bio, and notes about where internal links should go.
Step 5: We review it. Might be small tweaks, might be bigger edits. We’ll let you know what’s happening and when it goes live. Then you share it everywhere.
That’s it.
Why We Actually Need You
Here’s the truth. Automation ES exists because people like you share their knowledge. Every article helps someone. It may help them finally understand how neural networks work. Maybe it stops them from wasting weeks on the wrong approach. Perhaps it inspires them tactually to build something
Tech information is everywhere, but much of it is overcomplicated, overhyped, or just inaccurate. Deep learning is too important to get inadequate explanations. We’re trying to be different. Honest. Actually helpful.
If you care about helping people understand AI and deep learning and want an audience that actually gets it and will take action, this is the place. I genuinely think what you know could change how someone approaches this technology.
Ready to write for us deep learning content? Send your pitch. We’re actually excited to see what you’ve got.