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Der PyTorch-Bauplan: Von den Grundlagen neuronaler Netze bis hin zur Implementierung
Paperback
Publisher: Independently Published
Published: Mar 16 2026
Pages: 264
Weight: 0.79
Height: 0.55 Width: 6.00 Depth: 9.00
Language: German
Neuronale Netze verstehen viele - aber nur wenige können sie sauber implementieren, trainieren und produktionsreif deployen.
Dieses Buch schlie t genau diese Lücke.
Der PyTorch-Bauplan führt Sie strukturiert von den mathematischen Grundlagen künstlicher neuronaler Netze bis zur professionellen Umsetzung mit PyTorch.
Kein oberflächlicher Überblick.
Kein Copy-Paste ohne Verständnis.
Sondern ein klarer, technischer Bauplan für echte Deep-Learning-Projekte.
Was Sie in diesem Buch lernen
Grundlagen neuronaler Netze verständlich erklärt
Lineare Algebra und Backpropagation praxisnah angewendet
Aufbau von Feedforward-, CNN- und RNN-Architekturen
Eigene Modelle mit PyTorch implementieren
Training, Optimierung und Hyperparameter-Tuning
Loss-Funktionen und Optimizer strategisch einsetzen
Datensätze vorbereiten und DataLoader effizient nutzen
GPU-Beschleunigung und Performance-Optimierung
Debugging und Modellinterpretation
Deployment und Export von Modellen
Vom Konzept zur echten Implementierung
Dieses Buch begleitet Sie Schritt für Schritt:
Tensoren verstehen und manipulieren
Autograd und Gradientenmechanik meistern
Eigene Trainingsschleifen schreiben
Transfer Learning praktisch anwenden
Overfitting erkennen und verhindern
Experimente systematisch strukturieren
Produktionsnahe Projektarchitektur aufbauen
Jede Architektur wird nicht nur verwendet - sondern von Grund auf verstanden.
Praxisprojekte
Bildklassifikation mit Convolutional Neural Networks
Zeitreihenanalyse mit Recurrent Networks
Textklassifikation mit Embeddings
Transfer Learning für kleine Datensätze
Mini-End-to-End-Deep-Learning-Projekt
Alle Beispiele sind technisch präzise, nachvollziehbar kommentiert und realitätsnah aufgebaut.
Für wen ist dieses Buch geeignet?
Data-Science-Einsteiger mit Python-Grundkenntnissen
Machine-Learning-Studierende
Entwickler, die Deep Learning praktisch umsetzen wollen
KI-Interessierte mit technischem Anspruch
Wenn Sie neuronale Netze nicht nur benutzen, sondern wirklich verstehen und implementieren möchten, ist dieses Buch Ihr technischer Leitfaden.
Bauen Sie Modelle systematisch.
Trainieren Sie effizient.
Implementieren Sie Deep Learning professionell - mit PyTorch.
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