fast.ai are now offering a free course entitled "Practical Deep Learning For Coders, Part 1" which will be taught by Jeremy Howard (Kaggle's #1 competitor 2 years running)
The course uses Python as the teaching language (specifically using the dynamic deep learning library PyTorch), and it's recommended that you have at least one year of coding experience.
In the course, you'll learn how to:
Set up your own GPU server in the cloud
Use the keras library in python to train and run deep learning models
Build, debug, and visualize a state of the art convolutional neural network (CNN) for recognizing images
Get great results even from small datasets, by using transfer learning and semi-supervized learning
Understand the components of a neural network, including activation functions, dense and convolutional layers, and optimizers
Build, debug, and visualize a recurrent neural network (RNN) for natural language processing (NLP), including creating a Gated Recurrent Unit (GRU) RNN from scratch in theano
Create and use specialized architectures for dealing with localization, multi-scale images, etc
Recognize and deal with over-fitting, by using data augmentation, dropout, batch normalization, and similar techniques
Build state of the art recommendation systems using neural-network based collaborative filtering
It's well worth reading through Jeremy's post which covers why they've chosen PyTorch over Keras and Tensorflow. He points out some really interesting and compelling points
For details on the course, visit fast.ai's website
Let us know how you get on!