Artificial Neural Networks with Tensorflow 2

Ann Architecture Machine Learning Projects

Produkt bewerten

Poornachandra Sarang

Verlag:

APRESS

Unsere-Artikel-Nr.: P35025488

EAN: 9781484261491

Erhältlich:

Nicht auf Lager

Zustellung: Fr, 25.09.2026

Versand: Kostenlos

CHF 100.–

Beschreibung

Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architecturesstarting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis. This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are. What You'll LearnDevelop Machine Learning Applications Translate languages using neural networks Compose images with style transfer Who This Book Is For Beginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies.

Spezifikationen

Sprache

Englisch

Autor

Poornachandra Sarang

Erscheinungsjahr

2020

Format

Buch (Softcover)

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