NOA Labs
AI & LLM · 6 free lessons

Deep Learning Tutorial for Beginners

Deep learning from first principles — neural networks, backpropagation, optimizers, CNNs, RNNs and attention, with runnable Python code.

📝 6 lessons ⏱️ ~26m reading 🎓 Beginner → Advanced 💻 Code examples
Start lesson 1 — free 🎤 Interview questions
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Deep Learning course syllabus

Follow the lessons in order — each one builds on the last. Tick them off as you go.

  1. Deep Learning Introduction What deep learning is, how it relates to machine learning and AI, why it took off, and where it is used today. 4 min
  2. Build a Neural Network from Scratch in Python Code a two-layer neural network with only NumPy, train it on XOR, and see forward pass, loss and gradients in action. 5 min
  3. Backpropagation, Gradient Descent & Optimizers How backpropagation computes gradients with the chain rule, and how SGD, momentum and Adam use them to train networks. 5 min
  4. Convolutional Neural Networks (CNNs) How CNNs see images: convolutions, filters, pooling, feature maps and the architecture of a classic image classifier. 4 min
  5. RNNs, LSTMs & the Attention Mechanism How networks handle sequences, why RNNs struggle with long text, how LSTMs help, and how attention led to transformers. 4 min
  6. Deep Learning Interview Questions Common deep learning interview questions on neural networks, training, regularization, CNNs and transformers, with answers. 4 min

Deep Learning FAQ

Is this Deep Learning tutorial free?
Yes. All 6 Deep Learning lessons are free to read online with no sign-up. Each one includes working code examples you can copy and run.
Is Deep Learning good for beginners?
Yes. The tutorial starts with "Deep Learning Introduction" and builds up one concept at a time, so you don't need any prior Deep Learning experience — just follow the lessons in order.
How long does it take to learn Deep Learning?
Reading all 6 lessons takes about 1 hour. Most learners need a few weeks of hands-on practice alongside the lessons to feel confident using Deep Learning on real projects.
What should I learn after Deep Learning?
Good next topics are Artificial Intelligence, AI Agents, LLM Fine-Tuning, Generative AI. The AI Engineer Roadmap shows the full order.