Module 11: Advanced Deep Learning & Computer Vision
Go beyond basic neural networks with transfer learning, CNN architectures, sequence models, GANs and YOLO.
How to use this module: follow the lessons in order if the stack is new to you, or jump directly to the topic that matches your project. Each lesson includes runnable examples, references and a downloadable notebook.
Transfer Learning
Transfer Learning with PyTorch & Keras
CNN Architectures
CNN Architectures: VGG, ResNet & Inception
RNN & LSTM
RNN & LSTM Tutorial for Sequence Models
Autoencoders & GANs
Autoencoders & GANs with PyTorch
YOLO Detection
YOLO Object Detection Tutorial in Python
Mask Detection Project
Face Mask Detection Project with OpenCV
Practice strategy
Run the examples locally, change one assumption at a time, and record what changes in output, latency, memory or model quality. For production topics, add one failure test before you call the exercise complete.
Next step
Return to the full Data Science curriculum to continue with the next module.
