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Kubernetes Design Patterns and Extensions [eLearning]

This course shows how to exploit the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

What you will learn

  • Learn about machine learning landscapes along with the historical development and progress of deep learning
  • Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x
  • Access public datasets and utilize them using TensorFlow to load, process, and transform data
  • Use TensorFlow on real-world datasets, including images, text, and more
  • Learn how to evaluate the performance of your deep learning models
  • Using deep learning for scalable object detection and mobile computing
  • Train machines quickly to learn from data by exploring reinforcement learning techniques
  • Explore active areas of deep learning research and applications

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

  • Processor: i5/i7 2.6 GHz or higher
  • Memory: 8GB RAM
  • Hard disk: 50GB or more
  • An Internet connection

Software requirements

You’ll also need the following software installed in advance:

  • Access to a Kubernetes cluster with a version equal to or higher than 1.10
  • Local Kubernetes solutions such as minikube or clusters living in cloud providers:
  • The Kubernetes command-line tool kubectl is required for accessing Kubernetes from the Terminal
  • The Docker client and server with a minimum version of 18.03 are required for building and testing the client libraries
  • Python and Go installation is not required but is recommended for playing around with the client libraries locally