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Understand how neural networks work and learn how to implement them
using TensorFlow 2.0 and Keras. This new edition focuses on the
fundamental concepts and at the same time on practical aspects of
implementing neural networks and deep learning for your research
projects. This book is designed so that you can focus on the parts
you are interested in. You will explore topics as regularization,
optimizers, optimization, metric analysis, and hyper-parameter
tuning. In addition, you will learn the fundamentals ideas behind
autoencoders and generative adversarial networks. All the code
presented in the book will be available in the form of Jupyter
notebooks which would allow you to try out all examples and extend
them in interesting ways. A companion online book is available with
the complete code for all examples discussed in the book and
additional material more related to TensorFlow and Keras. All the
code will be available in Jupyter notebook format and can be opened
directly in Google Colab (no need to install anything locally) or
downloaded on your own machine and tested locally. You will: *
Understand the fundamental concepts of how neural networks work *
Learn the fundamental ideas behind autoencoders and generative
adversarial networks * Be able to try all the examples with
complete code examples that you can expand for your own projects *
Have available a complete online companion book with examples and
tutorials. This book is for: Readers with an intermediate
understanding of machine learning, linear algebra, calculus, and
basic Python programming.
Develop and optimize deep learning models with advanced
architectures. This book teaches you the intricate details and
subtleties of the algorithms that are at the core of convolutional
neural networks. In Advanced Applied Deep Learning, you will study
advanced topics on CNN and object detection using Keras and
TensorFlow. Along the way, you will look at the fundamental
operations in CNN, such as convolution and pooling, and then look
at more advanced architectures such as inception networks, resnets,
and many more. While the book discusses theoretical topics, you
will discover how to work efficiently with Keras with many tricks
and tips, including how to customize logging in Keras with custom
callback classes, what is eager execution, and how to use it in
your models. Finally, you will study how object detection works,
and build a complete implementation of the YOLO (you only look
once) algorithm in Keras and TensorFlow. By the end of the book you
will have implemented various models in Keras and learned many
advanced tricks that will bring your skills to the next level. What
You Will Learn See how convolutional neural networks and object
detection work Save weights and models on disk Pause training and
restart it at a later stage Use hardware acceleration (GPUs) in
your code Work with the Dataset TensorFlow abstraction and use
pre-trained models and transfer learning Remove and add layers to
pre-trained networks to adapt them to your specific project Apply
pre-trained models such as Alexnet and VGG16 to new datasets Who
This Book Is For Scientists and researchers with
intermediate-to-advanced Python and machine learning know-how.
Additionally, intermediate knowledge of Keras and TensorFlow is
expected.
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