Description:


By: Karl Whitford
Released: Thursday, January 31, 2019 New Release!
Torrent Contains: 25 Files
Course Source: https://www.packtpub.com/application-development/unsupervised-clustering-mesos-integrated-course

This is a hands-on course which helps learn soliton cluster isolation system for unsupervised clustering in Mesos

Video Details

ISBN 9781788479677
Course Length 2 hour 27 minutes

Table of Contents

• MESOS METRICS EXTRACTION
• DEEP NEURAL NETWORKS
• INITIALIZATION OF NEURONS
• ENSEMBLE PRUNING
• POST-PROCESSING
• SELF-OPTIMIZATION
• THE EXPERT ADVISOR
• THIRD GENERATION NEURAL NETWORKS

Video Description

Apache Mesos is an open source cluster manager that handles workloads in a distributed environment through dynamic resource sharing and isolation. Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.
This course begins with an introduction to Inference matroids wherein you will learn about vertex combiners with Hama, Graph Isomorphism, Soliton, and DAGs. Then you will learn to perform granular synthesis with druid streams and to write custom isolator module for Mesos. Next, you will be introduced to RoBo and will learn to manifold the cluster trees . Then you will understand what Pythonic Clojars and Monads are. Further, you will become familiar with the actor dining model and port mappings. Finally, you will learn to auto-scale clusters.

The code files are placed on GitHub at this link https://github.com/PacktPublishing/-Unsupervised-Clustering-in-Mesos

Style and Approach

This course will teach you graph cohomology for network isolation as a counterexample to a subcoloring NP-Hard problem of incredible importance at Netflix: resource allocation for Robust Bayes, PCA, or Ensemble learning to answer questions pertaining to the customer. You will learn about the Soliton Cluster isolation system, and, along with Hama, Storm, and a proprietary Pregel-Mesos API, you'll turn Mesos into the main building block of your own SPS for automated ML. Taking the concept of a graph topology to the next level, you will learn the cohomology of Fibonacci trees on manifolds.

What You Will Learn

• Get familiar with Inference matroids
• Learn graph isomorphism
• Learn how to perform granular synthesis with druid streams
• Understand how to write a custom isolator module for Mesos
• Learn to perform MCMC anomaly detection
• Get introduced to RoBo
• Learn to manifold cluster trees
• Understand what Pythonic Clojars and Monads are
• Learn about the Actor Dining Model and port mappings
• Learn how to perform cluster auto-scaling

Authors

Karl Whitford

Karl Whitford has been involved in the tech industry for 10 years as a software engineer. He has a background in statistical machine learning, deep learning, and A.I. from Columbia University. He also has knowledge of computational physics/mathematics from DePaul University and UT Austin. He is a professional in the fields of game A.I, compression, machine learning, and distributed cluster computing. Karl is an open source contributor to SMACK, Pancake Stack (PipelineI/O), and Pregel-Mesos, among others. He has previous work experience with Microsoft, Coca Cola, and Unilever to name a few; he is also an indie game developer and founder of Esquirel (Black-Squirrel) Studios in San Francisco, California. He was also handpicked by UploadVR as "one to watch" and featured at Mountain View’s 2016 VR Showcase.