Preparing and architecting for machine learning (handouts) by Netcom Learning

Preparing and Architecting for Machine Learning

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Agenda • Introduction to Machine Learning • Integrating Machine Learning with Big Data • Understanding Machine Learning and Robotics • Programming a Machine • Top AI technologies and how to identify the best fit

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Understanding the difference between Analytics, Machine Learning, and Artificial Intelligence

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TL;DR (The very short summary)

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Classical ML: Tasks

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Classical ML: Features

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Types of Machine Learning â&#x20AC;˘ Supervised learning, where observations contain input/output pairs (aka labeled data): These sample pairs are used to "train" the machine learning system to recognize certain rules for correlating inputs to outputs. Examples include types of ML that are trained to recognize a shape based on a series of shapes in pictures. â&#x20AC;˘ Unsupervised learning, where those labels are omitted: In this form of ML, rather than being "trained" with sample data, the machine learning system finds structures and patterns in the data on its own. Examples include types of ML that recognize patterns in attributes from input data that can be used to make a prediction or classify an object. â&#x20AC;˘ Reinforcement learning, where evaluations are given about how good or bad a certain situation is: Examples include types of ML that enable computers to learn to play games or drive vehicles https://www.netcomlearning.com/webinars/?advid=1315

Machine Learning vs. Programming

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Deep Learning

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Machine Learning Programming

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Features/Attributes of Data

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Integrating Machine Learning with Big Data

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Data Ingestion and ELT

Distributed Storage, Compute, and Reporting

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Machine Learning Architecture

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Understanding Machine Learning and Robotics 1 – Computer Vision Robot vision is very closely linked to machine vision, which can be given credit for the emergence of robot guidance and automatic inspection systems. The slight difference between the two may be in kinematics as applied to robot vision, which encompasses reference frame calibration and a robot’s ability to physically affect its environment. https://www.youtube.com/watch?v=eQLcDmfmGB0

2 – Imitation Learning Imitation learning is closely related to observational learning, a behavior exhibited by infants and toddlers. Imitation learning is also an umbrella category for reinforcement learning, or the challenge of getting an agent to act in the world so as to maximize its rewards. Bayesian or probabilistic models are a common feature of this machine learning approach. https://player.vimeo.com/video/92149421

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3 – Self-Supervised Learning Self-supervised learning approaches enable robots to generate their own training examples in order to improve performance; this includes using a priori training and data captured close range to interpret “long-range ambiguous sensor data.” It’s been incorporated into robots and optical devices that can detect and reject objects (dust and snow, for example); identify vegetables and obstacles in rough terrain; and in 3D-scene analysis and modeling vehicle dynamics (https://youtu.be/cLUCYPWi9Xo)

4 – Assistive and Medical Technologies An assistive robot (according to Stanford’s David L. Jaffe) is a device that can sense, process sensory information, and perform actions that benefit people with disabilities and seniors (though smart assistive technologies also exist for the general population, such as driver assistance tools). Movement therapy robots provide a diagnostic or therapeutic benefit. 5 – Multi-Agent Learning Coordination and negotiation are key components of multi-agent learning, which involves machine learning-based robots (or agents – this technique has been widely applied to games) that are able to adapt to a shifting landscape of other robots/agents and find “equilibrium strategies.” Examples of multi-agent learning approaches include noregret learning tools, which involve weighted algorithms that “boost” learning outcomes in multi-agent planning, and learning in market-based, distributed control systems.

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Programming a Machine

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Top AI technologies and how to identify the best fit

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H2O.ai H2O.ai, which is based in Mountain View, California, U.S., offers an open-source machine-learning platform. For this Magic Quadrant, we evaluated H2O Flow, its core component; H2O Steam; H2O Sparkling Water, for Spark integration; and H2O Deep Water, which provides deep-learning capabilities. H2O.ai has progressed from Visionary in the prior Magic Quadrant to Leader. It continues to progress through significant commercial expansion, and has strengthened its position as a thought leader and an innovator. Databricks Databricks is based in San Francisco, California, U.S. It offers the Apache Spark-based Databricks Unified Analytics Platform in the cloud. In addition to Spark, it provides proprietary features for security, reliability, operationalization, performance and real-time enablement on Amazon Web Services (AWS). Databricks announced a Microsoft Azure Databricks platform for preview in November 2017, which is not considered in this Magic Quadrant because it was not generally available at the time of evaluation.

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Recorded Webinar Video To watch the recorded webinar video for live demos, please access the link: https://bit.ly/2sDJcUM

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About NetCom Learning

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THANK YOU !!!

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