Practical Deep Learning for Coders - Full Course

Embark on a comprehensive 11-hour deep learning journey designed for coders with at least a year of programming experience, preferably in Python. Dive into practical applications of deep learning through eight lessons covering topics from building your first modules to natural language processing. Explore evidence and p-values, production and deployment, stochastic gradient descent from scratch, data ethics, collaborative filtering, tabular data, and more. Access accompanying book chapters and code on Google Colab for hands-on practice. Benefit from the expertise of Jeremy Howard, former president of Kaggle, and Sylvain Gugger, a seasoned researcher and math textbook author. Utilize the course website for additional resources, questionnaires, and set-up guides to enhance your learning experience.

Lesson 1 - Your first modules.
Lesson 2 - Evidence and p values.
Lesson 3 - Production and Deployment.
Lesson 4 - Stochastic Gradient Descent (SGD) from scratch.
Lesson 5 - Data ethics.
Lesson 6 - Collaborative filtering.