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The Applied TensorFlow and Keras Workshop
book

The Applied TensorFlow and Keras Workshop

by Harveen Singh Chadha, Luis Capelo, Abhranshu Bagchi, Achint Chaudhary, Vishal Chauhan, Alexis Rutherford, Subhash Sundaravadivelu
July 2020
Intermediate to advanced
174 pages
3h 1m
English
Packt Publishing

Overview

The Applied TensorFlow and Keras Workshop introduces you to the fundamentals of building and deploying deep learning models using TensorFlow and Keras. Through hands-on learning, you will understand neural networks, train and optimize models, and deploy them as applications. By the end, you'll have developed a practical Bitcoin price prediction tracker and acquired key data science skills.

What this Book will help me do

  • Understand the basics of neural networks and learn how to configure and train them.
  • Explore TensorFlow and Keras frameworks to implement deep learning solutions effectively.
  • Build a real-world application that predicts the price of Bitcoin using a trained model.
  • Learn to evaluate machine learning models and apply techniques to optimize their performance.
  • Develop and deploy machine learning models as interactive web applications with Flask.

Author(s)

Harveen Singh Chadha and Luis Capelo bring their extensive experience in machine learning, software development, and data science to this workshop. They have worked on a variety of real-world applications, giving them unique insights into practical and user-friendly deep learning solutions. Their teaching style is focused on hands-on, approachable learning, making complex concepts accessible.

Who is it for?

This book is ideal for data scientists and machine learning enthusiasts looking to deepen their knowledge of TensorFlow and Keras. It is suggested that readers have some familiarity with core computer science concepts and experience analyzing data. This workshop is particularly beneficial for those who wish to design, train, and deploy machine learning models in practical applications.

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Publisher Resources

ISBN: 9781800201217Supplemental Content