Ilan Zerath’s Post

Demystifying Transfer Learning: Making Machines Learn Faster Imagine if you could get an A+ on a test without ever cracking open the textbook for that subject. Sounds like magic, right? Well, in the machine learning world, transfer learning tries to do just that by letting models learn from what others have already learned. Instead of starting from scratch and spending ages training a model, transfer learning borrows knowledge from pre-trained models to speed things up....

If you, like me, were feeling buried by AI hype and didn't know where to actually start with Claude — here's the cheat code: The team that built Claude just put their entire training library online. For free. 17 courses. 5 learning tracks. ~20 hours of content. Zero $$. A few that stood out for someone in ops/payroll like me: → Claude 101 — the no-fluff intro to what Claude can actually do → Introduction to Claude Cowork — for automating multi-step work on real files → Introduction to Agent Skills — reusable instruction sets so you stop rewriting the same prompt every time → Building with the Claude API (8+ hours) — for when you're ready to wire it into your own tools → Introduction to Model Context Protocol — connect Claude to the systems you already use Each one ends in an official Anthropic certificate you can post on LinkedIn. No API key needed for most. No subscription. Just an email. Link: anthropic.skilljar.com If you've been waiting for a sign to stop scrolling AI threads and start actually learning, this is it. Gotta catch em all!

Free AI engineering course 👇 I took everything I taught in my (sunsetted) AI Builders Bootcamp and turned it into a YouTube course. It's a roadmap where each video builds on the last: 1) Fundamentals - LLM basics and how to use them 2) AI coding - how to use LLMs to build more, better, faster 3) Prompt Engineering - getting more out of any model 4) RAG - grounding models in reality 5) Agents - giving LLMs tools so they get things done 6) Evals - measuring if your AI system actually does what you want 7) Fine-tuning - adapting models to specific use cases via more training 8) Deploying AI Applications - shipping it to real users 👉 Check it out here: https://lnkd.in/gsqM9GAB

Started learning Rust. Trying to see what all the hype is about and 1 thing I can say is that I think it's super cool how they have an entire introductory book available for free for learners. In the age of AI where lookup is a few button types away, I love the opportunity to still slow down and READ and DIGEST information at a realistic pace simply for the joy of learning.

Everything you need to (master) Claude AI is free. Most people don't know where to look. Here's the complete stack: Start with these videos → Mastering Claude Code in 30 minutes → Full 1-hour course, build and automate anything → 36 beginner tips, zero to pro fast → Ultimate Claude 4.1 guide for 2026 GitHub Repos to bookmark → Claude Code, official Anthropic repo → Claude Cookbooks, ready to use recipes → Awesome Claude Skills, community built → Claude Agent SDK, build your own agents → Loki Mode, for the curious builders Guides worth reading → Claude Prompting Best Practices → Complete Guide to Building Claude Skills → Claude's Constitution, understand how it thinks Books if you want depth → Mastering Claude AI, prompts to pro → AI Engineering by Chip Huyen → Designing Machine Learning Systems Papers for serious learners → ReAct, how agents reason → Chain-of-Thought, the foundation → Tree-of-Thoughts, smarter AI thinking Link to doc: https://lnkd.in/daW-Kndv Save this post. Repost ♻️ so others find this. Check my profile for more AI resources 👋

In this day and age, the hardest skill isn't writing code — it's knowing whether the code you're reading is actually right. AI has fundamentally changed how fast we can build. But speed without understanding is just technical debt waiting to happen. Here's where I've landed: use AI to accelerate your learning, not replace it. When AI generates code, don't just run it — read it. Ask yourself: - Do I understand what this does line by line? - Could I debug this at 2am when it breaks in production? - Could I explain this to someone else? If the answer is no, you haven't finished learning yet. The engineers who will win long-term aren't the ones who prompt the best. They're the ones who can take AI-generated code, own it, and fix it when it fails. Fundamentals are more important than ever! Still figuring this out myself — but I'd rather be the engineer who understands slower than the one who ships fast and breaks everything.

Starting a course always feels easier than it turns out to be. 😅 You watch the instructor, copy what they do, hit run — and expect things to just work. Then the first error shows up. ❌ - Paste the error into an LLM — doesn't fix it 🤖 - Dig through the docs — still nothing 📄 - Search if anyone else ran into the same wall — barely anything useful 🔍 - Hours pass. Sometimes a whole day disappears into one problem. ⏳ You planned to finish a module. You could barely get through 2 videos. But that's actually where the learning happens — not in the smooth parts where everything works, but in the grinding, frustrating, stuck parts. That moment you finally get it working and write it down — it stays with you. No amount of watching someone else do it could have given you that. 💡 If a course never gets you stuck, it's probably not teaching you much. Have you ever spent hours stuck on one error — only to realize that one problem taught you more than the rest of the course combined? 😄 #Learning #Developers #AI #SoftwareDevelopment #TechSkills

I've been building courses alongside my client work for the past few months. Eight of them. Claude, Claude Code, AI workflows, the Anthropic API, MCP servers, subagents, agent skills. What surprised me wasn't the production work. It was having to name things I'd been doing on autopilot. When you've shipped twelve production AI systems, you develop intuitions without vocabulary. The agentic loop. The delegation matrix. The difference between what MCP tools, resources, and prompts are actually for. These things exist in your hands before they exist in your head. Writing a course forces you to reverse-engineer your own thinking. Module by module. Lesson by lesson. That process surfaced frameworks I didn't know I had. The courses are live. I'll be sharing specific ideas from them over the coming weeks — the kind of content that doesn't fit in a LinkedIn post but matters if you're working seriously with AI. More soon.

Traditional studying is broken. And it might be your fault. I sit for the SIE exam this Saturday. I spent weeks studying the traditional way. Lectures, PowerPoints, 281-page textbook, generic quizzes... You know the drill. But here's the problem: Lectures assume everyone learns at the same pace. PowerPoints assume everyone's weaknesses are the same. Textbooks assume you have unlimited time. So 3 days ago, I stopped. Instead, I uploaded all my study materials into AI and had it tailor everything to me. My learning style? ✅ My weak areas? ✅ My schedule? ✅ A study regimen built around you, in real time. One you can actually have a conversation with. It's a private tutor without the private tutor price tag. I've learned and retained more in 3 days than I did in the 3 weeks before it. So if you're still grinding through the traditional methods and not seeing results, there's a better way. And it's sitting right in front of you. The only question is whether you'll use it.