Applied neural networks with TensorFlow 2 : API oriented deep learning with Python

Bibliographische Detailangaben

Titel
Applied neural networks with TensorFlow 2 API oriented deep learning with Python
verantwortlich
Yalçın, Orhan Gazi (VerfasserIn)
veröffentlicht
New York, NY: Apress, [2021]
© 2021
Erscheinungsjahr
2021
Erscheint auch als
Yalçın, Orhan, Applied Neural Networks with TensorFlow 2, 1st edition, [Erscheinungsort nicht ermittelbar] : Apress, 2020, 1 online resource (306 pages)
Erscheint auch als
Yalçın, Orhan Gazi, Applied Neural Networks with TensorFlow 2, New York : Apress, 2021, 1 Online-Ressource (XIX, 295 Seiten)
Andere Ausgaben
Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python
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Zusammenfassung
Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations. You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy—others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you’ll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you’ll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. You will: Compare competing technologies and see why TensorFlow is more popular Generate text, image, or sound with GANs Predict the rating or preference a user will give to an item Sequence data with recurrent neural networks.
Umfang
xix, 295 Seiten; Illustrationen, Diagramme
Sprache
Englisch
Schlagworte
Maschinelles Lernen > Deep learning > TensorFlow
BK-Notation
54.72 Künstliche Intelligenz
ISBN
9781484265123