Practical Deep Learning For Coders—18 hours of lessons for free
- The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.
- If you can code, you can do deep learning
- It is very hands-on and adopts a top-down approach, which means everyone irrespective of varying knowledge can get started with implementing Deep learning models immediately.
- I now have the tools to apply deep learning models to real world problems.
- If you are looking to venture into the Deep learning field, look no further and take this course.
fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!
@jeremyphoward: After months of work, it’s done! Presenting the “Deep Learning For Coders” MOOC
18 hours of lessons, all free
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.
If you are looking to venture into the Deep learning field, look no further and take this course. It is very hands-on and adopts a top-down approach, which means everyone irrespective of varying knowledge can get started with implementing Deep learning models immediately. Another major factor why this course is very appealing is its emphasis on social relevance. That is, how can we use this awesome technology to serve the world better?
I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it! Jeremy is an incredible instructor and is able to make what might seem like a difficult subject completely accessible.
Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms. But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
Running a company is extremely time intensive, so I was a weary of taking on the commitment of the course. It was definitely worth it, though. It smashed my preconceptions about the technological obstructions to doing deep learning, and showed again and again examples where just a small subset of the training data and just a few epochs of training on standard GPU hardware could get most of the way towards a really good model