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Hands-On Convolutional Neural Networks with TensorFlow - Solve computer vision problems with modeling in TensorFlow and Python (Paperback)
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Hands-On Convolutional Neural Networks with TensorFlow - Solve computer vision problems with modeling in TensorFlow and Python (Paperback)
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Learn how to apply TensorFlow to a wide range of deep learning and
Machine Learning problems with this practical guide on training
CNNs for image classification, image recognition, object detection
and many computer vision challenges. Key Features Learn the
fundamentals of Convolutional Neural Networks Harness Python and
Tensorflow to train CNNs Build scalable deep learning models that
can process millions of items Book DescriptionConvolutional Neural
Networks (CNN) are one of the most popular architectures used in
computer vision apps. This book is an introduction to CNNs through
solving real-world problems in deep learning while teaching you
their implementation in popular Python library - TensorFlow. By the
end of the book, you will be training CNNs in no time! We start
with an overview of popular machine learning and deep learning
models, and then get you set up with a TensorFlow development
environment. This environment is the basis for implementing and
training deep learning models in later chapters. Then, you will use
Convolutional Neural Networks to work on problems such as image
classification, object detection, and semantic segmentation. After
that, you will use transfer learning to see how these models can
solve other deep learning problems. You will also get a taste of
implementing generative models such as autoencoders and generative
adversarial networks. Later on, you will see useful tips on machine
learning best practices and troubleshooting. Finally, you will
learn how to apply your models on large datasets of millions of
images. What you will learn Train machine learning models with
TensorFlow Create systems that can evolve and scale during their
life cycle Use CNNs in image recognition and classification Use
TensorFlow for building deep learning models Train popular deep
learning models Fine-tune a neural network to improve the quality
of results with transfer learning Build TensorFlow models that can
scale to large datasets and systems Who this book is forThis book
is for Software Engineers, Data Scientists, or Machine Learning
practitioners who want to use CNNs for solving real-world problems.
Knowledge of basic machine learning concepts, linear algebra and
Python will help.
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