PyTorch: The Ultimate Course from Beginner to Advanced - Part5

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🎥 Want to understand how neural networks actually draw decision boundaries? Or curious about how to handle binary vs multi-class classification in PyTorch? In this fifth part of our PyTorch Tutorial Course, we’ll build and train classification models on fun 2D datasets—circles, moons, blobs, and even spirals—to see how non-linear networks separate data.

We’ll start with simple linear baselines, then add ReLU activations to give models the power to curve and twist their decision boundaries. Along the way, you’ll implement binary cross-entropy for two-class problems, cross-entropy loss for multi-class, and track accuracy with torchmetrics. We’ll also re-create common activations like ReLU, Sigmoid, and Tanh from scratch, and visualize them to build intuition. Finally, we’ll tackle the famous spiral dataset, a classic non-linear challenge, and watch how a small MLP untangles it.

💻 Code on GitHub: https://github.com/frezazadeh/Pytorch-Tutorial/blob/main/Pytorch_Part5.ipynb

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🔖 Chapters & Timestamps

00:00:00 0. Quick Reference: Binary vs Multi-class Architecture
00:06:34 1. Circles Dataset – Linear Baseline
00:15:34 2. Adding Non-Linearity with ReLU
00:18:00 3. Custom Activations: ReLU, Sigmoid, Tanh from scratch
00:20:27 4. Multi-Class Classification with Blobs
00:23:15 5. Exercise: Moons Dataset Challenge (≥96% accuracy)
00:25:17 6. Advanced Challenge: Multi-Class Spirals

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📚 What You’ll Learn
• Binary vs Multi-class – How to set up outputs, activations, and loss functions correctly
• Visualization – Plotting points, decision boundaries, and activation functions
• Training Skills – Writing clean training loops with PyTorch
• Metrics – Using torchmetrics for binary and multi-class accuracy
• Hands-On Practice – Circles, Moons, Blobs, and Spirals datasets
• Intuition – Why non-linearity matters and how ReLU, Sigmoid, and Tanh shape learning

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✅ Why Watch This Video?
• Beginner Friendly – Step-by-step explanations with visuals
• Practical – See how decision boundaries actually form during training
• Comprehensive – Covers both binary and multi-class setups
• Fun – Work with visually intuitive toy datasets before moving to real-world data

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👍 If you found this helpful, please:

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💬 Join the conversation:
Which dataset visualization was most eye-opening for you—circles, moons, blobs, or spirals?
What kind of PyTorch challenge should we tackle in the next tutorial?

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#PyTorch #DeepLearning #MachineLearning #Python #AI #PyTorchTutorial #Classification #NeuralNetworks #Torchmetrics

Видео PyTorch: The Ultimate Course from Beginner to Advanced - Part5 канала BrainOmega