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Perform supervised and unsupervised machine learning and learn
advanced techniques such as training neural networks. Key Features
Train your own models for effective prediction, using high-level
Keras API Perform supervised and unsupervised machine learning and
learn advanced techniques such as training neural networks Get
acquainted with some new practices introduced in TensorFlow 2.0
Alpha Book DescriptionTensorFlow is one of the most popular machine
learning frameworks in Python. With this book, you will improve
your knowledge of some of the latest TensorFlow features and will
be able to perform supervised and unsupervised machine learning and
also train neural networks. After giving you an overview of what's
new in TensorFlow 2.0 Alpha, the book moves on to setting up your
machine learning environment using the TensorFlow library. You will
perform popular supervised machine learning tasks using techniques
such as linear regression, logistic regression, and clustering. You
will get familiar with unsupervised learning for autoencoder
applications. The book will also show you how to train effective
neural networks using straightforward examples in a variety of
different domains. By the end of the book, you will have been
exposed to a large variety of machine learning and neural network
TensorFlow techniques. What you will learn Use tf.Keras for fast
prototyping, building, and training deep learning neural network
models Easily convert your TensorFlow 1.12 applications to
TensorFlow 2.0-compatible files Use TensorFlow to tackle
traditional supervised and unsupervised machine learning
applications Understand image recognition techniques using
TensorFlow Perform neural style transfer for image hybridization
using a neural network Code a recurrent neural network in
TensorFlow to perform text-style generation Who this book is
forData scientists, machine learning developers, and deep learning
enthusiasts looking to quickly get started with TensorFlow 2 will
find this book useful. Some Python programming experience with
version 3.6 or later, along with a familiarity with Jupyter
notebooks will be an added advantage. Exposure to machine learning
and neural network techniques would also be helpful.
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