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Deep Learning Quick Reference - Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras (Paperback)
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Deep Learning Quick Reference - Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras (Paperback)
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Dive deeper into neural networks and get your models trained,
optimized with this quick reference guide Key Features A quick
reference to all important deep learning concepts and their
implementations Essential tips, tricks, and hacks to train a
variety of deep learning models such as CNNs, RNNs, LSTMs, and more
Supplemented with essential mathematics and theory, every chapter
provides best practices and safe choices for training and
fine-tuning your models in Keras and Tensorflow. Book
DescriptionDeep learning has become an essential necessity to enter
the world of artificial intelligence. With this book deep learning
techniques will become more accessible, practical, and relevant to
practicing data scientists. It moves deep learning from academia to
the real world through practical examples. You will learn how
Tensor Board is used to monitor the training of deep neural
networks and solve binary classification problems using deep
learning. Readers will then learn to optimize hyperparameters in
their deep learning models. The book then takes the readers through
the practical implementation of training CNN's, RNN's, and LSTM's
with word embeddings and seq2seq models from scratch. Later the
book explores advanced topics such as Deep Q Network to solve an
autonomous agent problem and how to use two adversarial networks to
generate artificial images that appear real. For implementation
purposes, we look at popular Python-based deep learning frameworks
such as Keras and Tensorflow, Each chapter provides best practices
and safe choices to help readers make the right decision while
training deep neural networks. By the end of this book, you will be
able to solve real-world problems quickly with deep neural
networks. What you will learn Solve regression and classification
challenges with TensorFlow and Keras Learn to use Tensor Board for
monitoring neural networks and its training Optimize
hyperparameters and safe choices/best practices Build CNN's, RNN's,
and LSTM's and using word embedding from scratch Build and train
seq2seq models for machine translation and chat applications.
Understanding Deep Q networks and how to use one to solve an
autonomous agent problem. Explore Deep Q Network and address
autonomous agent challenges. Who this book is forIf you are a Data
Scientist or a Machine Learning expert, then this book is a very
useful read in training your advanced machine learning and deep
learning models. You can also refer this book if you are stuck
in-between the neural network modeling and need immediate
assistance in getting accomplishing the task smoothly. Some prior
knowledge of Python and tight hold on the basics of machine
learning is required.
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