This course focuses on practical deep learning for coders in 2019. By the end of the course, learners will be able to implement image classification, data cleaning, production, segmentation, NLP, collaborative filtering, and more. The course teaches skills such as SGD from scratch, back propagation, regularization, and building neural nets from scratch. The teaching method includes hands-on coding exercises and implementing algorithms from scratch. This course is intended for coders and individuals interested in deep learning and neural networks.
Overview
Syllabus
Lesson 1: Deep Learning 2019 - Image classification.
Lesson 2: Deep Learning 2019 - Data cleaning and production; SGD from scratch.
Lesson 3: Deep Learning 2019 - Data blocks; Multi-label classification; Segmentation.
Lesson 4: Deep Learning 2019 - NLP; Tabular data; Collaborative filtering; Embeddings.
Lesson 5: Deep Learning 2019 - Back propagation; Accelerated SGD; Neural net from scratch.
Lesson 6: Deep Learning 2019 - Regularization; Convolutions; Data ethics.
Lesson 7: Deep Learning 2019 - Resnets from scratch; U-net; Generative (adversarial) networks.
Taught by
Jeremy Howard
