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Artificial Neural Networks with TensorFlow 2 - ANN Architecture Machine Learning Projects (Paperback, 1st ed.)
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Artificial Neural Networks with TensorFlow 2 - ANN Architecture Machine Learning Projects (Paperback, 1st ed.)
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Develop machine learning models across various domains. This book
offers a single source that provides comprehensive coverage of the
capabilities of TensorFlow 2 through the use of realistic,
scenario-based projects. After learning what's new in TensorFlow 2,
you'll dive right into developing machine learning models through
applicable projects. This book covers a wide variety of ANN
architectures-starting from working with a simple sequential
network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full
chapter is devoted to each kind of network and each chapter
consists of a full project describing the network architecture
used, the theory behind that architecture, what data set is used,
the pre-processing of data, model training, testing and performance
optimizations, and analysis. This practical approach can either be
used from the beginning through to the end or, if you're already
familiar with basic ML models, you can dive right into the
application that interests you. Line-by-line explanations on major
code segments help to fill in the details as you work and the
entire project source is available to you online for learning and
further experimentation. With Artificial Neural Networks with
TensorFlow 2 you'll see just how wide the range of TensorFlow's
capabilities are. What You'll Learn Develop Machine Learning
Applications Translate languages using neural networks Compose
images with style transfer Who This Book Is For Beginners,
practitioners, and hard-cored developers who want to master machine
and deep learning with TensorFlow 2. The reader should have working
concepts of ML basics and terminologies.
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