deep-learning

I’m giving a seminar about deep learning in medical and biological image and video segmentation, organized by the Machine Learning Community (MLC) Dresden. I’ll be giving an introduction into the field and a hands-on tutorial. The event should be suitable for beginners as well as more advanced users in deep learning. Deep learning has a great potential for biomedical image analysis. In particular, convolutional neural networks are an important class of machine learning techniques that can be trained to classify, detect, localize, and segment objects or abnormalities in different image modalities by learning to extract relevant image features.

classifying tumors using proteomics, imaging and deep learning

detection of HER2 amplification status from FISH images

This hands-on course will take you from 0 to 100 in Deep Learning with Keras. Our aim is to teach the fundamentals of deep learning with Convolutional Neural Networks (CNN) based on modern techniques using the Keras API and the Tensorflow backend. By the end participants will know how to build deep learning models, how to train them, what to avoid during training, what to check during training and how to perform model inference, especially for image based problems.

Problem Suppose you found your favorite data set on Kaggle, but it is multiple gigabytes and you need it on your deep learning machine, not your local laptop. You cannot simply use wget because you need to be logged in to Kaggle. Solution The solution is to export your cookies and tell wget to use your cookies when downloading the data. To export your cookies, install the chrome extension called cookietxt-export and do the following: