VEĽKÝ VÝBER
Ponúkame milióny kníh v angličtine. Od beletrie až po tie najodbornejšie odborné.
ISBN | 9783030703905 |
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Autor | McClarren Ryan G. |
Vydavatel | Springer Nature |
Jazyk | english |
Väzba | Paperback |
Rok vydania | 2022 |
Počet strán | 247 |
Part I Fundamentals
1.0 Introduction
1.1. Where machine learning can help engineers
1.2. Where machine learning cannot help engineers1.3. Machine learning to correct idealized models
2. The Landscape of machine learning
2.1. Supervised learning
2.1.1. Regression
2.1.2. Classification
2.1.3. Time series
2.1.4. Reinforcement
2.2. Unsupervised Learning2.3. Optimization
2.4. Bayesian statistics
2.5. Cross-validation3. Linear Models
3.1. Linear regression
3.2. Logistic regression
3.3. Regularized regression
3.4. Case Study: Determining physical laws using regularized regression
4. Tree-Based Models
4.1. Decision Trees
4.2. Random Forests4.3. BART
4.4. Case Study: Modeling an experiment using random forest models
5. Clustering data
5.1. Singular value decomposition
5.2. Case Study: SVD to standardize several time series
5.3. K-means
5.4. K-nearest neighbors
5.5. t-SNE
5.6. Case Study: The reflectance spectrum of different foliage
Part II Deep Neural Networks
6. Feed-Forward Neural Networks
6.1. Neurons6.2. Dropout
6.3. Backpropagation
6.4. Initialization6.5. Regression
6.6. Classification
6.7. Case Study: The strength of concrete as a function of age and ingredients7. Convolutional Neural Networks
7.1. Convolutions
7.2. Pooling
7.3. Residual networks
7.4. Case Study: Finding volcanoes on Venus
8. Recurrent neural networks for time series data
8.1. Basic Recurrent neural networks
8.2. Long-term, Short-Term memory8.3. Attention networks
8.4. Case Study: Predicting future system performance
Part III Advanced Topics in Machine Learning9. Unsupervised Learning with Neural Networks
9.1. Auto-encoders
9.2. Boltzmann machines9.3. Case study: Optimization using Inverse models
10. Reinforcement learning
10.1. Case study: controlling a mechanical gantry
11. Transfer learning
11.1. Case study: Transfer learning a simulation emulator for experimental measurementsPart IV Appendices
A. SciKit-Learn
B. Tensorflow