So I’ve been wondering what problem to work on for my learning while I work my way through #practicaldeeplearning. I think I’ve found the perfect dataset: https://figshare.com/collections/_/4560497
It’s got everything. The raw 12-lead ECG readings as well as arrhythmia classifications for over 10,000 patients. And best of all it’s easy to convert the ECG readings to images for #deeplearning. Will probably start by trying out #ResNet initially.
Will keep posting my progress here.
#practicaldeeplearning #deeplearning #ResNet
Sometimes when creating shortcuts on #ResNet people use a MaxPooling2D or even a convolution.
If you want to pass the identity of your input, you can use a Convolution (with no gradient) initialized with dirac_ (delta function A.K.A identity function for a convolution).
As you see is faster than using a MaxPooling:
#NeuralNetwork #PyTorch
#ResNet #neuralnetwork #pytorch
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep ...
Jarrod Haas, William Yolland, Bernhard T Rabus
To share more than just an introduction post, here's a fun bit of research that I was involved with during the 2019 FDL programme - https://www.climatechange.ai/papers/neurips2019/11 - #ResNet #CloudPhysics #FDL #MODIS #ClimateChange #iResNet #CloudClassification #GCP
#ResNet #CloudPhysics #FDL #MODIS #climatechange #iResNet #CloudClassification #GCP