Video Description
This is a hands-on course which helps learn soliton cluster isolation system for unsupervised clustering in Mesos
About This Video
- In-depth coverage of Inference matroids
- Perform granular synthesis with druid streams
- Write a custom isolator module for Mesos
- Perform MCMC anomaly detection
- Understand the actor dining model and docker port mappings
In Detail
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
Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
Table of Contents
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Chapter 1 : Mesos Metrics Extraction
- The Course Overview 00:00:54
- Mesos Initial Setup 00:03:11
- Exploratory Data Analysis 00:09:01
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Chapter 2 : Deep Neural Networks
- Outlier Detection 00:07:14
- Sample Selection and Dimensionality Reduction 00:07:30
- Chapter 3 : Initialization of Neurons
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Chapter 4 : Ensemble Pruning
- Continuous Prediction 00:07:28
- Stacking 00:09:20
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Chapter 5 : Post-Processing
- Extreme Learning Machine 00:07:22
- The SuperEnsemble 00:10:06
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Chapter 6 : Self-Optimization
- Genetic Algorithms 00:08:54
- Simulated Annealing 00:09:44
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Chapter 7 : The Expert Advisor
- Grafana with Webhook 00:05:57
- Self-Control and Self-Training 00:08:03
- Keras and TensorFlow 00:12:06
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Chapter 8 : Third Generation Neural Networks
- MLP versus DLVQ versus Jordan versus Elman 00:12:56
- Error Correction and Boltzmann Rule 00:07:31
- Prioritizing Attention 00:08:08