Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow : Konzepte, Tools und Techniken für intelligente Systeme
Language: German
Published by Dpunkt.Verlag Sep 2023, 2023
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 47.36
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Add to basketItem description from seller
Neuware - Behandelt jetzt viele neue Features von Scikit-Learn sowie die Keras-Tuner-Bibliothek und die NLP-Bibliothek Transformers von Hugging Face Führt Sie methodisch geschickt in die Basics des Machine Learning mit Scikit-Learn ein und vermittelt darauf aufbauend Deep-Learning-Techniken mit Keras und TensorFlow Mit zahlreiche Übungen und Lösungen Maschinelles Lernen und insbesondere Deep Learning haben in den letzten Jahren eindrucksvolle Durchbrüche erlebt. Inzwischen können sogar Programmierer, die kaum etwas über diese Technologie wissen, mit einfachen, effizienten Werkzeugen Machine-Learning-Programme implementieren. Dieses Standardwerk verwendet konkrete Beispiele, ein Minimum an Theorie und unmittelbar einsetzbare Python-Frameworks (Scikit-Learn, Keras und TensorFlow), um Ihnen ein intuitives Verständnis der Konzepte und Tools für das Entwickeln intelligenter Systeme zu vermitteln. In dieser aktualisierten 3. Auflage behandelt Aurélien Géron eine große Bandbreite von Techniken: von der einfachen linearen Regression bis hin zu Deep Neural Networks. Zahlreiche Codebeispiele und Übungen helfen Ihnen, das Gelernte praktisch umzusetzen. Sie benötigen lediglich etwas Programmiererfahrung, um direkt zu starten. Lernen Sie die Grundlagen des Machine Learning anhand eines umfangreichen Beispielprojekts mit Scikit-Learn Erkunden Sie zahlreiche Modelle, einschließlich Support Vector Machines, Entscheidungsbäume, Random Forests und Ensemble-Methoden Nutzen Sie unüberwachtes Lernen wie Dimensionsreduktion, Clustering und Anomalieerkennung Erstellen Sie neuronale Netzarchitekturen wie Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Autoencoder, Diffusionsmodelle und Transformer Verwenden Sie TensorFlow und Keras zum Erstellen und Trainieren neuronaler Netze für Computer Vision, Natural Language Processing, Deep Reinforcement Learning und generative Modelle.…
Seller Inventory # 9783960092124
- Title
- Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow : Konzepte, Tools und Techniken für intelligente Systeme
- Author
- Aurélien Géron
- Publisher
- Dpunkt.Verlag Sep 2023
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- German
- ISBN 10
- 3960092121
- ISBN 13
- 9783960092124
- Item weight
- 1,408 grams
- Dimensions
- 239x163x44 mm
- Now covers many new features from Scikit-Learn, as well as Hugging Face's Keras Tuner Library and Transformers NLP Library
- Methodically introduces you to the basics of machine learning with Scikit-Learn and conveys deep learning techniques with Keras and TensorFlow
- With numerous exercises and solutions.
Machine learning and deep learning in particular have experienced impressive breakthroughs in recent years. Meanwhile, even programmers who know little about this technology can implement machine learning programs with simple, efficient tools. This standard work uses concrete examples, a minimum of theory, and immediate Python frameworks (Scikit-Learn, Keras, and TensorFlow) to give you an intuitive understanding of the concepts and tools for developing intelligent systems.
In this updated 3rd Edition, Aurélien Géron covers a wide range of techniques: from simple linear regression to deep neural networks. Numerous code examples and exercises help you implement what you have learned in practice. All you need is some programming experience to start directly.
- Learn the basics of machine learning through an extensive sample project with Scikit-Learn
- Explore numerous models including Support Vector Machines, Decision Trees, Random Forests, and Ensemble Methods
- Use unsupervised learning such as dimension reduction, clustering and anomaly detection
- Create neural network architectures such as convolutional neural networks, recurrent neural networks, generative adversarial networks, autoencoders, diffusion models and transformers
- Use TensorFlow and Keras to create and train neural networks for computer vision, natural language processing, deep reinforcement learning, and generative models
"Synopsis" may belong to another edition of this title.
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