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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Hands-On Neural Networks with Keras - Design and create neural networks using deep learning and artificial intelligence principles (Paperback)
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Hands-On Neural Networks with Keras - Design and create neural networks using deep learning and artificial intelligence principles (Paperback)
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Your one-stop guide to learning and implementing artificial neural
networks with Keras effectively Key Features Design and create
neural network architectures on different domains using Keras
Integrate neural network models in your applications using this
highly practical guide Get ready for the future of neural networks
through transfer learning and predicting multi network models Book
DescriptionNeural networks are used to solve a wide range of
problems in different areas of AI and deep learning. Hands-On
Neural Networks with Keras will start with teaching you about the
core concepts of neural networks. You will delve into combining
different neural network models and work with real-world use cases,
including computer vision, natural language understanding,
synthetic data generation, and many more. Moving on, you will
become well versed with convolutional neural networks (CNNs),
recurrent neural networks (RNNs), long short-term memory (LSTM)
networks, autoencoders, and generative adversarial networks (GANs)
using real-world training datasets. We will examine how to use CNNs
for image recognition, how to use reinforcement learning agents,
and many more. We will dive into the specific architectures of
various networks and then implement each of them in a hands-on
manner using industry-grade frameworks. By the end of this book,
you will be highly familiar with all prominent deep learning models
and frameworks, and the options you have when applying deep
learning to real-world scenarios and embedding artificial
intelligence as the core fabric of your organization. What you will
learn Understand the fundamental nature and workflow of predictive
data modeling Explore how different types of visual and linguistic
signals are processed by neural networks Dive into the mathematical
and statistical ideas behind how networks learn from data Design
and implement various neural networks such as CNNs, LSTMs, and GANs
Use different architectures to tackle cognitive tasks and embed
intelligence in systems Learn how to generate synthetic data and
use augmentation strategies to improve your models Stay on top of
the latest academic and commercial developments in the field of AI
Who this book is forThis book is for machine learning
practitioners, deep learning researchers and AI enthusiasts who are
looking to get well versed with different neural network
architecture using Keras. Working knowledge of Python programming
language is mandatory.
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