PyTorch, The Practical Guid, Gollnick B., 2026

PyTorch, The Practical Guid, Gollnick B., 2026.

   This book is aimed at a broad target group interested in deep learning with PyTorch, including students of computer science, mathematics, and related disciplines as well as professionals who want to expand their knowledge in this area. The book is written in such a way that a beginner in machine learning can work with it, but experienced professionals can also use it to improve their skills and knowledge.




Independent and Dependent Features.
The terms independent features and dependent features refer to the roles that variables (columns) play in a dataset. This concept is primarily used in supervised learning.

Independent features are also called input variables, predictors, or characteristics, and they are the inputs for ML models. It’s assumed that independent features are the causes of or influencing factors for the dependent variable. In statistics, dependent features are also called target variables, output variables, or labels, and they are the output values that are ultimately predicted by the model.

The ML model thus learns the relationships or patterns that exist between the independent and dependent variables, and after it has been trained, it can use this knowledge to predict future values of the dependent variable based on new values of the independent features.

Contents.
Notes on Usage.
Preface.
Target Group.
Requirements.
Structure of the Book.
How to Use This Book.
Downloading Code and Additional Materials.
Preparing the System.
Acknowledgements.
Conventions Used in This Book.
1 Introduction to Deep Learning.
2 Creating Your First PyTorch Model.
3 Classification Models.
4 Computer Vision.
5 Recommendation Systems.
6 Autoencoders.
7 Graph Neural Networks.
8 Time Series Forecasting.
9 Language Models.
10 Pretrained Networks and Fine-Tuning.
11 PyTorch Lightning.
12 Model Evaluation, Logging, and Monitoring.
13 Deployment The Author.
Index.
Service Pages.
Legal Notes.



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