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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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Structuring Your Problem

Compared to researchers, practitioners spend much less time determining which architecture to choose when starting a new deep learning project. Acquiring data that represents a given problem correctly is the most important factor to consider when developing these systems, followed by an understanding of the dataset's inherent biases and limitations. When starting to develop a deep learning system, consider the following questions for reflection:

  • Do I have the right data? This is the hardest challenge when training a deep learning model. First, define your problem with mathematical rules. Use precise definitions and organize the problem into either categories (classification problems) or a continuous scale (regression problems). Now, how can you collect data pertaining to those metrics?
  • Do I have enough data? Typically, deep learning algorithms have shown to perform much better on large datasets than on smaller ones. Knowing how much data is...
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The Applied TensorFlow and Keras Workshop
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