Concepts from this tag are mostly base on the fastai book and course. They serve as an excellent entry point to deep learning. The pedagogical approach of the course is to de-emphasize math and focus instead on building models with code. The lessons focus on building neural network to solve NLP, tabular data, computer vision and collaborative filtering problems. Each lesson covers a similar set of steps (in different application domains) that takes you from basic EDA to training a neural net to perform a task.
My notes are my own walkthrough of the core concepts in the book, stripping out dependencies on the fastai library and fill in the steps that are not clear to me in the book. The dataset come from the fastai library. Each form a runnable notebook. You can find the original jupyter notebooks here.