Skip to content
Home Online-Ressourcen Data Science
Empfohlene Online-Ressourcen aus dem Bereich Data Science
Bücher (Online-Books):
Podcasts:
Blogs:
Artikel:
Comprehensive list of activation functions in neural networks with pros/cons (Phylliida, 2015), A list of cost functions used in neural networks, alongside applications (Phylliida, 2018), Efficient BackProp (Yann LeCun et al., 1998), Introduction to Gradient Descent Algorithm (along with variants) in Machine Learning (Faizan Shaikh, 2017), Deep Sparse Rectifier Neural Networks (Xavier Glorot et al., 2011), A Neural Network in 13 lines of Python (Part 2 – Gradient Descent) – Improving our neural network by optimizing Gradient Descent (Andrew Trask, 2015), Gradient-Based Learning Applied to Document Recognition (Yann LeCun et al., 1998), Understanding Convolutional Neural Networks with A Mathematical Model (C.-C. Jay Kuo, 2016), Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (Kaiming He et al., 2015), Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition (Dominik Scherer et al., 2010), The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) (Adit Deshpande, 2016), A Friendly Introduction to Cross-Entropy Loss (Rob DiPietro, 2016), Mit Turing und Heidegger über künstliche Intelligenz nachdenken (NZZ, 2019)
Datensätze (frei verfügbar):
Kaggle : mehr als 16.000 Datasets aus den verschiedensten Bereichen, iNaturalist.org : Tierbeobachtungen, weltweit The Australian Election Study (Forschungsprojekt): long-term perspective on stability and change in the political attitudes and behaviour of the Australian electorate. Zusammenfassende Darstellungen
