Practical Deep Learning - Schedule

M from Germany - just getting started with ML and deeplearning. Working on theoretical quantum many body physics.
SH from Stockholm. I work in Agrigenomics and am using Deep Learning for image analysis.
S from Sweden. Working in Pharmacogenomics. No experience in DL but interested for future projects.
V from Germany. I want to get a better understanding of ML and deepLearning. Working on theoretical quantum many body physics
V from Lund, Sweden. Working on phage genomics and protein structures. Vastly experienced in ML but hoping to learn more about Deep learning as this is an approach I would use for my project in the next months.
A, Sweden. Computer Vision + ML package for digitizing tables containing handwritten-text.
M, Recognition of plant diseases with Pytorch.
T, Prediction of biophysical properties from protein dynamics
V from Poland, working on a university project and thought deep learning on supercomputers might help stand out. Just starting out with HPC in general.
I from Sweden, currently don't use deep learning but think it's good to be in the know. Work in quantum computing.
R, Sweden. Learn about machine and deep learning for materials research.
D, ML interatomic potentials
A, from Saudi Arabia, working on Density Functional Theory for my master research
S, Sweden. Working with Pattern Detection and Analysis of Emmision data from IC engines
K, Sweden. Working with model based calibration for IC engines
G, Germany. I would like to know how to classify patterns for materials.

Schedule

Time	Contents	Instructor(s)
09:00-09:10	Welcome	YW
09:10-09:50	Introduction to Deep Learning	YW
09:50-10:00	Coffee Break
10:00-10:50	Classification by a neural network using Keras (I)	AM
10:50-11:00	Coffee Break
11:00-11:50	Classification by a neural network using Keras (II)	AM
11:50-12:00	Wrap-up

Time	Contents	Instructor(s)
09:00-09:10	Welcome and recap	YW
09:10-10:00	Monitor the training process	YW
10:00-10:10	Coffee Break
10:10-11:00	Advanced Layer Types	AM
11:10-11:10	Coffee Break
11:10-11:50	Transfer learning & Outlook	AM
11:50-12:00	Wrap-up

Time	Contents	Instructor(s)
09:00-09:10	Welcome and recap	YW
09:10-10:20	Three tools (Geneformer, Boltz-1, GROMACS) to get your feet wet with computational drug development	SM
10:20-10:40	Coffee Break
10:40-11:50	Applications of Deep Learning algorithms for retinal diseases diagnosis based on Optical Coherence Tomography imaging	EP
11:50-12:00	Wrap-up	YW

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