Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python


Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python
By 作者:Orhan Gazi Yalçın
pages 页数: 316 pages
ISBN-10 : 1484265122
ISBN-13 : 9781484265123
Dimensions : 6.1 x 0.72 x 9.25 inches
Publisher Finelybook 出版社 : Apress; 1st ed. edition (November 30, 2020)
Language 语言: : English
The Book Description robot was collected from Amazon and arranged by Finelybook
Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations.
You’ll start By 作者: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 Learn

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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