Python Machine Learning, 3rd Edition S Raschka, V Mirjalili Packt Publishing Ltd., 2019 | 1810* | 2019 |
Python Machine Learning S Raschka Packt Publishing, 2015 | 1758* | 2015 |
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning S Raschka arXiv preprint arXiv:1811.12808, 2018 | 1245 | 2018 |
MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack S Raschka Journal of open source software 3 (24), 638, 2018 | 649 | 2018 |
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence S Raschka, J Patterson, C Nolet Information 11 (4), 193, 2020 | 546 | 2020 |
Naive Bayes and Text Classification I - Introduction and Theory S Raschka arXiv preprint arXiv:1410.5329, 2014 | 268 | 2014 |
Rank consistent ordinal regression for neural networks with application to age estimation W Cao, V Mirjalili, S Raschka Pattern Recognition Letters 140, 325-331, 2020 | 249 | 2020 |
Machine Learning with PyTorch and Scikit-Learn S Raschka, YH Liu, V Mirjalili Packt Publishing Ltd., 2022 | 129 | 2022 |
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images V Mirjalili, S Raschka, A Namboodiri, AR Ross 11th IAPR International Conference on Biometrics (ICB 2018), 2018 | 122 | 2018 |
An Overview of General Performance Metrics of Binary Classifier Systems S Raschka arXiv preprint arXiv:1410.5330, 2014 | 110 | 2014 |
About feature scaling and normalization S Raschka Sebastian Raschka. Disques, nd Web. Dec, 2014 | 102* | 2014 |
Linear Discriminant Analysis bit by bit S Raschka Blog, August, 2014 | 93* | 2014 |
PrivacyNet: Semi-adversarial networks for multi-attribute face privacy V Mirjalili, S Raschka, A Ross IEEE Transactions on Image Processing 29, 9400-9412, 2020 | 91 | 2020 |
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition S Raschka, B Kaufman Methods 180, 89-110, 2020 | 68 | 2020 |
Pdf Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, by S Raschka | 64 | 2017 |
Machine Learning mit Python und Scikit-learn und TensorFlow: das umfassende Praxis-Handbuch für Data Science, Deep Learning und Predictive Analytics S Raschka, V Mirjalili, K Lorenzen mitp, 2018 | 62* | 2018 |
BioPandas: Working with molecular structures in pandas DataFrames S Raschka The Journal of Open Source Software 2 (14), 2017 | 57 | 2017 |
Protein–ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and … S Raschka, AJ Wolf, J Bemister-Buffington, LA Kuhn Journal of computer-aided molecular design 32, 511-528, 2018 | 53 | 2018 |
Gender privacy: An ensemble of semi adversarial networks for confounding arbitrary gender classifiers V Mirjalili, S Raschka, A Ross 2018 IEEE 9th International Conference on Biometrics Theory, Applications …, 2018 | 51 | 2018 |
FlowSAN: Privacy-enhancing Semi-Adversarial Networks to Confound Arbitrary Face-based Gender Classifiers V Mirjalili, S Raschka, A Ross IEEE Access, 2019 | 50 | 2019 |