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The Applied TensorFlow and Keras Workshop

The Applied TensorFlow and Keras Workshop

By : Harveen Singh Chadha , Luis Capelo
4.5 (2)
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The Applied TensorFlow and Keras Workshop

The Applied TensorFlow and Keras Workshop

4.5 (2)
By: Harveen Singh Chadha , Luis Capelo

Overview of this book

Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy. As a beginner, you’ll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you’ve understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you’ll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you’ll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you’ll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization. By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects.
Table of Contents (6 chapters)
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Hyperparameter Optimization

So far, we have trained a neural network to predict the next 7 days of Bitcoin prices using the preceding 76 weeks of prices. On average, this model issues predictions that are about 8.4 percent distant from real Bitcoin prices.

This section describes common strategies for improving the performance of neural network models:

  • Adding or removing layers and changing the number of nodes
  • Increasing or decreasing the number of training epochs
  • Experimenting with different activation functions
  • Using different regularization strategies

We will evaluate each modification using the same active learning environment we developed by the end of the Model Evaluation section, measuring how each one of these strategies may help us develop a more precise model.

Layers and Nodes – Adding More Layers

Neural networks with single hidden layers can perform fairly well on many problems. Our first Bitcoin model (bitcoin_lstm_v0) is a good...

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