This intensive workshop provides a comprehensive exploration of deep learning applications in life sciences, focusing on practical techniques for analyzing complex biological datasets. Participants will gain theoretical and hands-on experience with deep learning tools such as TensorFlow and Keras, learning to construct neural networks and apply them to areas like genomics and personalized medicine.

Topics Covered
Neural Network Basics , Coding neural network from scratch in plain Python and R , Big data in Biology , Single cell application , Data integration with neural networks , Deep Learning for ancient DNA applications , Deep Learning for microscopy imaging data , Convolutional neural networks for genomics and metagenomics , Autoencoder , Application for dimension reduction , Integration of single cell data , Coding autoencoder in Keras and TensorFlow , Comparison with tSNE and UMAP , Conding convolutional neural network in Keras , Applying CNN to nucleotide sequences , DNA text analysis with neural networks , NLP methods applied to DNA analysis , Personalized Medicine , Clinical diagnostics , Bayesian learning , Uncertainties , Prediction on unseen data , Variational Bayes , Markov Chain Monte Carlo (MCMC)