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),
- Introduction to Convolutional Neural Networks (Jianxin Wu, 2017),
- 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.
