Course: Deep Learning for Bioscientists

Week by week The main topics we will cover during this five-week course are as follows: Week 1 – introduction to deep learning: Recap of core machine learning concepts Machine …

A reminder on artificial neural networks. Before we go any further, its worth reminding ourselves about how ‘regular’ artificial neural networks work. You will have encountered these already if you …

Congratulations on completing the course deep learning for bioscientists! We’ve covered a lot during the course, in what is a relatively new and fast moving topic, but hope we have …

An example of using deep learning with PyTorch for image segmentation. In the linked Colab notebook we will again use the Oxford flower dataset and PyTorch to make a deep …

A short article that explains how the deep learning networks we’ve seen used for classification can also be used for regression-type problems. We’ve already seen how deep learning can be …

Congratulations on completing Week 5, the final week of the course. We gave a quick overview of quite a few different topics in deep learning this week, including: encoder-decoder architectures …

A closer look at some of the important innovations used in encoder network architectures. In the video we talked about a few different network architectures, including LeNet, VGG, ResNet and …

Congratulations on completing Week 4 of the course, and thanks for sticking with the course so far. You should know be familiar with some of the standard CNN architectures, and …

How do we adjust a CNN for use in a regression problem rather than classification? In the linked Colab notebook we will take the image classifier we created in the …

Well done – we are now more than halfway through the course! By now, we hope you have a basic working understanding of what convolutional neural networks are, how they …

To train a network, we need some way of evaluating its performance. So we need to consider what the network will output, and how we will compare that output to …

Congratulations on completing week two of the course! We covered a lot of important concepts this week, and we hope you have a better understanding of how convolutional neural networks, …

To use new image data we will need to make a custom dataset. In the linked Colab notebook we will put together everything we have learned so far to make …

To use new image data we will need to make a custom dataset. In the linked Colab notebook we will make a custom dataset in PyTorch based on the Oxford …

How to use datasets and dataloaders in PyTorch. In the linked Colab notebook we will demonstrate PyTorch datasets and dataloaders using the MNIST handwritten digits dataset included with PyTorch. We’ll …