Deep Learning Course

Welcome! Here we will upload the different tutorials for the Deep Learning course. We will use Python and Jupyter Notebook, and Keras as the deep learning framework. Also, the tutorials use Colaboratory, which is a free Jupyter notebook environment that runs in the cloud.

Each of the notebooks contains this image

which when clicked takes you to the Colaboratory website.

Colaboratory provides free GPU, so you can modify part of the tutorials and retrain the models to test your modifications. Specifically, you get 12 hours of continuous access to a k80 GPU. When those 12 hours are over, you can connect to another machine. This Notebook shows a comparison of the CPU vs GPU speed up in Colaboratory. The Hardware accelerator can be selected in Edit->Notebook Settings. There is also the option of using Tensor Processor Units (TPUs), but the tutorials will only use the GPU. Here is an example notebook using TPUs.

We will update the repository during the course with new tutorials.

Links to the notebooks

Sometimes the notebooks do not render correctly in GitHub. You can access directly the notebook in the Colab environment using the following links.

Tutorial 1

• Python part 1
• Python part 2
• Keras

Tutorial 2

• Introduction to CNNs

Tutorial 3

• Evaluation Measures

Tutorial 4

[04/02/20] Updated the tutorial removing the lr_normalizer line in the model_scan function when using talos, which was scaling the learning rate

• Network Training

Tutorial 5

[28/02/20] Removed Task 2, ResNet coding and training, from tutorial.

[1/03/20] Moved back to SGD optimiser instead of Adam in Tutorial 5.

• Introduction to Convolutional Neural Networks Architectures

Tutorial 6

• Recurrent Neural Networks (RNN)

Tutorial 7

• Autoencoders

Tutorial 8

• VAE-GAN

Tutorial 9

• Reinforcement Learning