PinnedArtem OppermanninTowards Data ScienceSelf Learning AI-Agents Part I: Markov Decision ProcessesA mathematical guide on the theory behind Deep Reinforcement Learning·11 min read·Oct 14, 2018--15--15

Artem OppermanninTowards Data ScienceSpeeding Up Training of Neural Networks with Batch-NormalizationOne of the most essential Key-Techniques in Deep Learning·8 min read·Mar 28, 2022----

Artem OppermannLet’s predict Human Behavior with AIUsing Deep Learning to predict how a customer will behave in the future·9 min read·Mar 24, 2022--2--2

Artem OppermannUnderfitting and Overfitting in Deep LearningNot sure if good model… or just overfitting?·10 min read·Jul 18, 2021----

Artem OppermannLoss Functions in Deep LearningA Guide on the Concept of Loss Functions in Deep Learning — What they are, Why we need them…·10 min read·Mar 7, 2021----

Artem OppermanninTowards Data ScienceActivation Functions in Deep Neural NetworksA Guide on the Theory of Activation Functions in Neural Networks and why we need them in the first place.·12 min read·Mar 1, 2021--1--1

Artem OppermanninDataSeriesArtificial Intelligence Market SizeAnalysis of the Technology of our Future — Trends, Projections, Opportunities·5 min read·Mar 1, 2021----

Artem OppermanninTowards Data ScienceStochastic-, Batch-, and Mini-Batch Gradient Descent DemystifiedWhy do we need Stochastic, Batch, and Mini Batch Gradient Descent when implementing Deep Neural Networks?·13 min read·Apr 26, 2020--4--4

Artem OppermanninTowards Data ScienceRegularization in Deep Learning — L1, L2, and DropoutA Guide on the Theory and Practicality of the most important Regularization Techniques in Deep Learning·9 min read·Feb 19, 2020--6--6

Artem OppermanninTowards Data ScienceAnomaly Detection with Autoencoders in TensorFlow 2.0A Guide on how to implement Neural Networks in TensorFlow 2.0 to detect anomalies.·15 min read·Jan 14, 2020--6--6