By Jeff Nickoloff

Docker in motion teaches readers easy methods to create, install, and deal with functions hosted in Docker packing containers. After beginning with a transparent clarification of the Docker version, you'll how you can package deal functions in packing containers, together with suggestions for checking out and allotting functions. additionally, you will how to run courses securely and the way to control shared assets. utilizing rigorously designed examples, the booklet teaches you ways to orchestrate packing containers and functions from deploy to elimination. alongside the way in which, you'll notice concepts for utilizing Docker on structures starting from dev-and-test machines to full-scale cloud deployments.

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This chapter describes a series of approximation techniques that are directly applicable for these problems. Chapter 14 There are many problems that can be described under the umbrella of “resource allocation” that offer special structure that we can exploit. These problems tend to be high-dimensional, with state variables that can easily have thousands or even millions of dimensions. However, when we combine concavity with the post-decision state variable, we produce algorithms that can handle industrial-strength applications.

Computer scientists, especially those working in the realm of artificial intelligence, found that dynamic programming was a useful framework for approaching certain classes of machine learning problems known as reinforcement learning. As different communities discovered the same concepts and algorithms, they invented their own vocabularies to go with them. As a result we can solve the Bellman equations, the Hamiltonian, the Jacobian, the Hamilton–Jacobian, or the allpurpose Hamilton–Jacobian–Bellman equations (typically referred to as the HJB equations).

We avoid many of the rich modeling and algorithmic issues that arise in more realistic problems. Chapter 1 Here we set the tone for the book, introducing the challenge of the three “curses of dimensionality” that arise in complex systems. Chapter 2 Dynamic programs are best taught by example. Here we describe three classes of problems: deterministic problems, stochastic problems, and information acquisition problems. Notation is kept simple but precise, and readers see a range of different applications.

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