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Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras

By : Purkait
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Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras

By: Purkait

Overview of this book

Neural 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.
Table of Contents (16 chapters)
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Free Chapter
1
Section 1: Fundamentals of Neural Networks
5
Section 2: Advanced Neural Network Architectures
10
Section 3: Hybrid Model Architecture
13
Section 4: Road Ahead

Loading pictures from a local directory

If you would like to follow along, simply google some nice leopard pictures and store them in a local directory. You can use the image loader from the utils module in Keras vis to resize your images to the target size that the ResNet50 model accepts (that is, images of 224 x 224 pixels):

Since we wish to make the experiment considerably arduous for our network, we purposefully selected pictures of camouflaged leopards to see how well this network does at detecting some of nature's most intricate attempts to hide these predatory creatures from the sight of prey, such as ourselves:

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