Advanced Deep Learning with PyTorch
A responsible PyTorch portfolio with tensors, autograd, training loops, CNNs, transfer learning, embeddings, transformers, diagnostics, and a model card.
What you will be able to do
A responsible PyTorch portfolio with tensors, autograd, training loops, CNNs, transfer learning, embeddings, transformers, diagnostics, and a model card.
Use a repeatable workflow instead of disconnected tips.
Check assumptions, evidence, output quality, and limitations before using results.
Create a capstone artefact that can be inspected, improved, and reviewed.
What you need before starting
Learners who want an independent PyTorch route after ML foundations or after TensorFlow/Keras basics.
Machine Learning Foundations and Applied Machine Learning Algorithms are strongly recommended.
Static lessons, activities, knowledge checks, learner templates, and a downloadable text-first pack.
12-module course sequence
The sequence follows the approved detailed syllabus and is implemented as a complete text-first shell.
Deep Learning Mindset, Boundaries, and Setup
- help learners understand when deep learning is worth using and when it is not.
- Text-first module with units, activity, knowledge check, and summary.
Tensors, Shapes, Datasets, and DataLoaders
- make learners fluent with the data structures used by neural networks.
- Text-first module with units, activity, knowledge check, and summary.
Autograd, Loss, Backpropagation, and Training Loops
- demystify how neural networks learn.
- Text-first module with units, activity, knowledge check, and summary.
Feed-Forward Networks for Structured Data
- connect neural networks to familiar tabular modelling problems.
- Text-first module with units, activity, knowledge check, and summary.
Optimization, Initialization, Normalization, and Regularization
- teach why training can fail and how to diagnose it.
- Text-first module with units, activity, knowledge check, and summary.
Convolutional Neural Networks for Images
- introduce computer vision through small, understandable models.
- Text-first module with units, activity, knowledge check, and summary.
Transfer Learning and Fine-Tuning for Vision
- show how pretrained models change the deep learning workflow.
- Text-first module with units, activity, knowledge check, and summary.
Embeddings, Sequences, and Attention Intuition
- prepare learners for transformer workflows without jumping too fast.
- Text-first module with units, activity, knowledge check, and summary.
Transformers for Text Classification and Feature Extraction
- teach practical transformer use while keeping claims honest.
- Text-first module with units, activity, knowledge check, and summary.
Representation Learning and Embedding Workflows
- connect deep learning representations to retrieval and clustering ideas.
- Text-first module with units, activity, knowledge check, and summary.
Diagnostics, Interpretability, Robustness, and Responsible Use
- prevent learners from trusting neural networks too quickly.
- Text-first module with units, activity, knowledge check, and summary.
Capstone Deep Learning Portfolio and Model Card
- bring the course together into a responsible final project.
- Text-first module with units, activity, knowledge check, and summary.
Text-first release boundary
The course content, static lessons, and download pack are available now. Leonardo/image assets, external specialist review, and learner pilot evidence remain planned improvements.