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Practical Deep Learning For Coders, Part 1

 with  Jeremy Howard

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. 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 during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.

Syllabus

  1. Recognizing cats and dogs
  2. Convolutional neural networks
  3. Why deep learning. Intro to convolutions
  4. CNN architecture basics. Avoiding over and under-fitting
  5. CNN/SGD in Excel. Pseudo-labeling. Collaborative filtering
  6. Intro to NLP, keras functional API, and RNNs
  7. Embeddings in Excel. Building RNNs
  8. Exotic CNN architecures. RNN from scratch
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Cost Free Online Course
Pace Self Paced
Institution fast.ai
Provider Independent
Language English
Hours 10 hours a week
Calendar 7 weeks long
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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) 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.

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