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

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 learn...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Yalçın, Orhan Gazi (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Berkeley, CA] : Apress, [2021]
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Yalçın, Orhan Gazi,  |e author. 
245 1 0 |a Applied neural networks with TensorFlow 2 :  |b API oriented deep learning with Python /  |c Orhan Gazi Yalçın. 
264 1 |a [Berkeley, CA] :  |b Apress,  |c [2021] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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347 |a text file 
347 |b PDF 
500 |a Includes index. 
520 |a 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 
505 0 |a Chapter 1: Introduction -- Chapter 2: Introduction to Machine Learning -- Chapter 3: Deep Learning and Neutral Networks Overview -- Chapter 4: Complimentary Libraries to TensorFlow 2.x -- Chapter 5: A Guide to TensorFlow 2.0 and Deep Learning Pipeline -- Chapter 6: Feedfoward Neutral Networks -- Chapter 7: Convolutional Neural Networks -- Chapter 8: Recurrent Neural Networks -- Chapter 9: Natural Language Processing -- Chapter 10: Recommender Systems -- Chapter 11: Auto-Encoders -- Chapter 12: Generative Adversarial Networks. 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed February 10, 2021). 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
630 0 0 |a TensorFlow. 
650 0 |a Machine learning. 
650 0 |a Neural networks (Computer science) 
650 0 |a Python (Computer program language) 
650 0 |a Artificial intelligence. 
650 2 |a Neural Networks, Computer 
650 2 |a Artificial Intelligence 
650 6 |a Apprentissage automatique. 
650 6 |a Réseaux neuronaux (Informatique) 
650 6 |a Python (Langage de programmation) 
650 6 |a Intelligence artificielle. 
650 7 |a artificial intelligence.  |2 aat 
650 7 |a Python (Computer program language)  |2 fast  |0 (OCoLC)fst01084736 
650 7 |a Neural networks (Computer science)  |2 fast  |0 (OCoLC)fst01036260 
650 7 |a Machine learning.  |2 fast  |0 (OCoLC)fst01004795 
650 7 |a Artificial intelligence.  |2 fast  |0 (OCoLC)fst00817247 
776 0 8 |i Printed edition:  |z 9781484265123 
776 0 8 |i Printed edition:  |z 9781484265147 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781484265130/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL6416135 
938 |a YBP Library Services  |b YANK  |n 17137940 
994 |a 92  |b IZTAP