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Neural Networks with Keras Cookbook

Neural Networks with Keras Cookbook

By : V Kishore Ayyadevara
3.3 (8)
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Neural Networks with Keras Cookbook

Neural Networks with Keras Cookbook

3.3 (8)
By: V Kishore Ayyadevara

Overview of this book

This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Table of Contents (18 chapters)
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Building a Deep Feedforward Neural Network

In this chapter, we will cover the following recipes:

  • Training a vanilla neural network
  • Scaling the input dataset
  • Impact of training when the majority of inputs are greater than zero
  • Impact of batch size on model accuracy
  • Building a deep neural network to improve network accuracy
  • Varying the learning rate to improve network accuracy
  • Varying the loss optimizer to improve network accuracy
  • Understanding the scenario of overfitting
  • Speeding up the training process using batch normalization

In the previous chapter, we looked at the basics of the function of a neural network. We also learned that there are various hyperparameters that impact the accuracy of a neural network. In this chapter, we will get into the details of the functions of the various hyperparameters within a neural network.

All the codes for this chapter are available at...

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