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Applied neural networks with TensorF...

Yalcin, Orhan Gazi.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied neural networks with TensorFlow 2/ by Orhan Gazi Yalcin.
其他題名:	API oriented deep learning with Python /
作者:	Yalcin, Orhan Gazi.
出版者:	Berkeley, CA :Apress : : 2021.,
面頁冊數:	xix, 295 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-6513-0
ISBN:	9781484265130

Applied neural networks with TensorFlow 2 = API oriented deep learning with Python /
Yalcin, Orhan Gazi.

Applied neural networks with TensorFlow 2API oriented deep learning with Python /[electronic resource] :by Orhan Gazi Yalcin. - Berkeley, CA :Apress :2021. - xix, 295 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484265130

Standard No.: 10.1007/978-1-4842-6513-0doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .Y35 2021

Dewey Class. No.: 006.31

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