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.
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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