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Python Deep Learning - Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition (Paperback, 2nd Revised edition)
Loot Price: R1,418
Discovery Miles 14 180
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Python Deep Learning - Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition (Paperback, 2nd Revised edition)
Expected to ship within 18 - 22 working days
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Learn advanced state-of-the-art deep learning techniques and their
applications using popular Python libraries Key Features Build a
strong foundation in neural networks and deep learning with Python
libraries Explore advanced deep learning techniques and their
applications across computer vision and NLP Learn how a computer
can navigate in complex environments with reinforcement learning
Book DescriptionWith the surge in artificial intelligence in
applications catering to both business and consumer needs, deep
learning is more important than ever for meeting current and future
market demands. With this book, you'll explore deep learning, and
learn how to put machine learning to use in your projects. This
second edition of Python Deep Learning will get you up to speed
with deep learning, deep neural networks, and how to train them
with high-performance algorithms and popular Python frameworks.
You'll uncover different neural network architectures, such as
convolutional networks, recurrent neural networks, long short-term
memory (LSTM) networks, and capsule networks. You'll also learn how
to solve problems in the fields of computer vision, natural
language processing (NLP), and speech recognition. You'll study
generative model approaches such as variational autoencoders and
Generative Adversarial Networks (GANs) to generate images. As you
delve into newly evolved areas of reinforcement learning, you'll
gain an understanding of state-of-the-art algorithms that are the
main components behind popular games Go, Atari, and Dota. By the
end of the book, you will be well-versed with the theory of deep
learning along with its real-world applications. What you will
learn Grasp the mathematical theory behind neural networks and deep
learning processes Investigate and resolve computer vision
challenges using convolutional networks and capsule networks Solve
generative tasks using variational autoencoders and Generative
Adversarial Networks Implement complex NLP tasks using recurrent
networks (LSTM and GRU) and attention models Explore reinforcement
learning and understand how agents behave in a complex environment
Get up to date with applications of deep learning in autonomous
vehicles Who this book is forThis book is for data science
practitioners, machine learning engineers, and those interested in
deep learning who have a basic foundation in machine learning and
some Python programming experience. A background in mathematics and
conceptual understanding of calculus and statistics will help you
gain maximum benefit from this book.
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