Advance your PyTorch skills by building sophisticated deep learning models and preparing them for deployment. You’ll design custom architectures that go beyond Sequential models, exploring Siamese Networks, ResNet, and DenseNet to understand how modern systems handle complex data. You’ll build Transformer architectures and explore how attention mechanisms power modern language models. You’ll also learn how diffusion models generate realistic images by reversing noise. Along the way, you’ll visualize model behavior using saliency maps and class activation maps, and prepare models for deployment with ONNX, MLflow, pruning, and quantization. By the end, you’ll be ready to create efficient, interpretable, and deployable PyTorch models for real-world deep learning tasks.
This course was so helpful in understanding the 'why' of the ML steps, not just the PyTorch itself.
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This is an excellent specialization taught by one of the best instructors/professionals in this field, Laurence Moroney. He has taught several highly rated courses on DeepLearning.ai, which I have taken so far. He provides practical insights that you can benefit from in your day-to-day work as a Data Scientist or AI/ML Engineer.
This course was so helpful in understanding the 'why' of the ML steps, not just the PyTorch itself.
This course scales in complexity so significantly from the first that it isn't realistic a new learner would be able to complete the assignments without either (a) spending months going through all the references and writing code separately or (b) just copy/pasting from the hints. It's too bad because the overview of architectures was cool, albeit really rushed. Wasted opportunity.