#15: Deep Learning and Transformers - the roadmap | Visibility > technical skills - why?
Reading time - 7 mins
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🧠 ML Section
Deep Learning & Transformers - the roadmap
💰 ML Section
Why visibility > technical skills in getting ML jobs
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2. Technical ML Section
Deep Learning & Transformers - the roadmap
People often try to learn deep learning by copying code from Hugging Face or running GPT-like models without understanding what’s going on inside.
That might work for toy projects, but in real-world systems, this lack of intuition becomes a bottleneck.
This is a 5-step roadmap that I recommend if you want to actually understand how deep learning and transformers work, and not just use them like a black box.
📌 Step 1: Learn Core Deep Learning Concepts
You need a solid foundation. That means you must understand how neural networks are trained, what backpropagation does, and how architectures like CNNs and RNNs process data.
Andrew Ng’s Deep Learning Specialization on Coursera is still one of the best resources for that.
The explanations are clear, math is digestible, and there’s a strong focus on intuition.
👉 Deep Learning Specialization (Coursera)
📌 Step 2: Internalize with Code
This is where you build intuition with code.
The fast.ai course flips the typical approach—you start coding real models right away using PyTorch, and the theory is introduced as needed.
It’s super practical and designed for people who want to build, not just study. You’ll train image classifiers, NLP models, and tabular networks from Day 1.
👉 fast.ai Deep Learning for Coders
📌 Step 3: Master PyTorch
If you’re building DL models, you’ll likely use PyTorch.
The syntax is clean, debugging is easy, and it's the standard in most research and production codebases today.
The official PyTorch tutorials are underrated.
They don’t just teach API usage, but they walk you through the internals of model building, training loops, gradient tracking, and module design.
You’ll write your own custom layers and learn to debug weird training issues, which is crucial for real-world work.
📌 Step 4: Understand Transformers Visually
Jay Alammar breaks down the transformer architecture step by step: token embeddings, positional encodings, attention heads, and more.
Once you go through his “Illustrated Transformer”, you stop just running BERT and start understanding what it’s doing.
👉 The Illustrated Transformer by Jay Alammar
📌 Step 5: Real-World Transformers
The Hugging Face course and docs are the most practical resource for applying transformers today.
You’ll learn how to fine-tune pre-trained models like BERT, GPT, and T5 on your own datasets.
It covers tokenization, training loops, evaluation metrics, and even deployment basics.
This is exactly the kind of work companies look for in NLP projects—and the stuff that gets you interview-ready too.
👉 Hugging Face Transformers Course
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2. ML Career Section
Why visibility > technical skills in getting ML jobs
Most ML folks are obsessed with one thing: becoming technically better.
Sure, learning new loss functions, reading another paper, tweaking another pipeline… all that matters.
But here’s the hard truth: these days, jobs don’t go to the best technical people. They go to the most visible.
If your name shows up a few times on LinkedIn - shared a post about your project or wrote a quick ML breakdown- chances are, a hiring manager or recruiter has already noticed you.
And that alone puts you ahead of 99% of candidates who stay invisible.
So what does visibility look like?
There are many possibilities. Here are a few of them:
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Posting a weekly LinkedIn breakdown of what you’re learning (even simple stuff)
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Sharing short write-ups of past projects.
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Creating a public roadmap of your ML learning and sticking to it.
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Publishing a practical tutorial that you wish had existed when you were learning
You don’t need to go viral. You need to be findable.
Because in today’s job market, being visible is what gets you into the room.
Your skills matter after that.
What to know how you can become more visible?
Book a 1:1 Career Session with me and we'll figure out a personalized path for you.
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