# Advanced topics in Deep Learning, ESPCI

This 3rd-year course is a follow up of an introduction to deep-learning in 2nd year. It covers some advanced topics in deep-learning with guest lectures and lab sessions in pytorch (python module). The plenary session include exercises ( or TD).

## News

• First course: January the 9th. Early in the morning !

## Expected schedules

It starts in january 2023 (the 9th). The course is scheduled on monday, starting at 8:30 in the morning.

• 9/01, course: introduction, reminder and exercises
• 16/01, course:
• Reminder - 2
• Pytorch
• 23/01, lab session 1
• Refresher on pytorch
• Convolution on CIFAR10
• 30/01, lab session 2
• ResNet
• 06/02, course:
• Sequence and convolution 1D
• Recurrent Network
• Project
• 13/02, lab session 3
• Recurrent Network
• 20/02, course:
• Generative models (TBC)
• 13/03, guest course: Applications to Biology

Vaitea Opuu

• 20/03, guest course: Deep Learning for Astro-Physics

David Cornu

• 27/03, guest course: Machine Learning, Physics and Fluid Mechanics

Lionel Mathelin

## python and Notebooks: how to

We will use python 3, pytorch and notebooks. If you need to work with own computer, there are 2 ways:

• use colab with a google account (the easiest, nothing todo)

from google.colab import drive

drive.mount('/content/gdrive')
# in my drive, I have a directory "Colab Notebooks"
# the dataset is uploaded there
root_path = 'gdrive/My Drive/Colab Notebooks/'


## Projects

Here, you can find a list of possible projects. Feel free to interact with me. For some of them, just ask me the data, otherwise a link is provided. Of course, you can also propose a project. This section is under construction and maybe some projects will be added as soon as I will have more feedbacks from my colleagues.

• Reconstruction of the vorticity field of a flow behind a cylinder from a handful sensors on the cylinder surface
• Chaos as an interpretable benchmark for forecasting and data-driven modelling
• Predicting the sequence specificities of DNA- and RNA-binding proteins. We can use datasets from Deep-bind.
• Deep sequence models for protein classification: there is a recent paper on this topic and data can be available. We can try different models (maybe simpler) for the same task.
• Chemistry: Predict the standard density of pure fluids, using a newly compiled database. From SMILES description, how can we predict density ? Ask me for the data and tools.
• Classify sleep and arousal stages from physiological signals including: electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG), and oxygen saturation (SaO2). See the challenge page for more details
• Classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified: The challenge page. Other datasets can be nice:
• Using seismic signals to predict the timing of laboratory earthquakes.
• Quantum-mechanical molecular energies prediction from the raw molecular geometry: see the QM7 database.
• Classify Molecule polarization: the data comes from time-lapse fluorescence microscopy images of the bacterium Pseudomonas fluorescens SBW25. Each image is an individual bacterial cell. These bacteria produce a molecule called pyoverdin which is naturally fluorescent, so the images show the distribution of this molecule inside the cells. We have discovered that there are two distribution patterns of this molecule: homogeneous, or accumulated at the cell pole ("polarized").