PinnedPublished inTDS ArchiveSelf Learning AI-Agents Part I: Markov Decision ProcessesA mathematical guide on the theory behind Deep Reinforcement LearningOct 14, 2018A response icon15Oct 14, 2018A response icon15
Published inTDS ArchiveSpeeding Up Training of Neural Networks with Batch-NormalizationOne of the most essential Key-Techniques in Deep LearningMar 28, 2022Mar 28, 2022
Let’s predict Human Behavior with AIUsing Deep Learning to predict how a customer will behave in the futureMar 24, 2022A response icon2Mar 24, 2022A response icon2
Underfitting and Overfitting in Deep LearningNot sure if good model… or just overfitting?Jul 18, 2021Jul 18, 2021
Loss Functions in Deep LearningA Guide on the Concept of Loss Functions in Deep Learning — What they are, Why we need them…Mar 7, 2021Mar 7, 2021
Published inTDS ArchiveActivation Functions in Deep Neural NetworksA Guide on the Theory of Activation Functions in Neural Networks and why we need them in the first place.Mar 1, 2021A response icon1Mar 1, 2021A response icon1
Published inDataSeriesArtificial Intelligence Market SizeAnalysis of the Technology of our Future — Trends, Projections, OpportunitiesMar 1, 2021Mar 1, 2021
Published inTDS ArchiveStochastic-, Batch-, and Mini-Batch Gradient Descent DemystifiedWhy do we need Stochastic, Batch, and Mini Batch Gradient Descent when implementing Deep Neural Networks?Apr 26, 2020A response icon4Apr 26, 2020A response icon4
Published inTDS ArchiveRegularization in Deep Learning — L1, L2, and DropoutA Guide on the Theory and Practicality of the most important Regularization Techniques in Deep LearningFeb 19, 2020A response icon6Feb 19, 2020A response icon6
Published inTDS ArchiveAnomaly Detection with Autoencoders in TensorFlow 2.0A Guide on how to implement Neural Networks in TensorFlow 2.0 to detect anomalies.Jan 14, 2020A response icon6Jan 14, 2020A response icon6
