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Applied Neural Networks with TensorFlow 2

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Produktnummer: 1883863b0a6b234d34b51e265216cc8dfa
Autor: Yalçin, Orhan Gazi
Themengebiete: AI API Artificial Intelligence DL Data Science Deep Learning ML Machine Learning TensorFlow programming
Veröffentlichungsdatum: 30.11.2020
EAN: 9781484265123
Sprache: Englisch
Seitenzahl: 295
Produktart: Kartoniert / Broschiert
Verlag: APRESS
Untertitel: API Oriented Deep Learning with Python
Produktinformationen "Applied Neural Networks with TensorFlow 2"
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.What You'll LearnCompare competing technologies and see why TensorFlow is more popularGenerate text, image, or sound with GANsPredict the rating or preference a user will give to an itemSequence data with recurrent neural networksWho This Book Is For Data scientists and programmers new to the fields of deep learning and machine learning APIs.
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