Deep Learning – Grundlagen und Implementierung

Fundamental principles and concepts of neural networks explained systematically and understandably. Basics of deep learning for data scientists and software developers with experience in machine learning. Implementation of deep learning concepts using the popular framework PyTorch. Numerous code examples in Python illustrate the concrete application.

This book provides you with the conceptual and practical tools to gain a deep understanding of how neural networks work. You will learn the fundamental principles of deep learning, illustrated by concept models developed by Seth Weidman, accompanied by code examples. The book is suitable for data scientists and software developers with experience in machine learning.

You will start with the basics of deep learning and quickly progress to the details of advanced architectures by implementing them from the ground up. You will become familiar with multilayer neural networks such as Convolutional and Recurrent Neural Networks. You will then practically apply these neural network concepts using the popular framework PyTorch. In this way, you will build a solid understanding of how neural networks function mathematically, computationally, and conceptually.