PinnedPublished inTowards Data ScienceThe Million-Dollar Question: When to Stop Training your Deep Learning ModelOn early stopping, or how to avoid overfitting or underfitting by knowing how long to train your neural network forJun 22, 20211Jun 22, 20211
PinnedPublished inTowards Data ScienceDealing with Imbalanced Data in TensorFlow: Class WeightsClass imbalance is a common challenge when training Machine Learning models. Here is a possible solution by generating class weights and…Jun 8, 20212Jun 8, 20212
Published inKlarna EngineeringStop Misusing ROC Curve and GINI: Navigate Imbalanced Datasets with ConfidenceDiscover how the Precision-Recall curve can provide a more robust metric for binary classification in data science and machine learning.Nov 9, 2023Nov 9, 2023
Published inTowards Data ScienceTaking the TensorBoard Embedding Projector to the Next LevelTensorBoard Projector allows to graphically represent low-dimensional embeddings. Here I show you how, instead of displaying a point, you…Jul 28, 20213Jul 28, 20213
Published inTowards Data ScienceStratified Sampling: You May Have Been Splitting Your Dataset All WrongRandomly generating splits of the data set is not always the optimal solution, as the proportions in the target variable can be extremely…Jul 21, 20211Jul 21, 20211
Published inTowards Data ScienceRemoving Duplicate or Similar Images in PythonWhen training a machine learning model that uses images as input, it is relevant to check for similar or duplicated pictures in the…Jul 14, 20212Jul 14, 20212
Published inTowards Data ScienceHow to Split a Tensorflow Dataset into Train, Validation, and Test setsJun 2, 2021Jun 2, 2021
Published inTowards Data ScienceA better comparison of TensorBoard experimentsAn existing TensorBoard limitation is that it only considers the last epoch when ranking experiments. Here is how to better evaluate…May 12, 2021May 12, 2021