This course assumes you are familiar with part 1, Practical Deep Learning for Coders, so head over there if you haven't completed that course, or are not already familiar with current deep learning best practices. We will be assuming familiarity with everything from part 1, such as: CNNs (including resnets), RNNs (including LSTM and GRU), SGD/Adam/etc, batch normalization, data augmentation, Keras, and numpy. Like in part 1, there are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded at The Data Institute at USF.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.