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173 Result(s)
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Key Concepts for Success with Intelligent Systems
This video segment introduces a pattern for organizing machine learning systems, including closing the loop between users and intelligence, and balancing the parts of the system so that they support one another.
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The Experience
This video segment explains how user experience and intelligence work together to achieve results, and how experience can support intelligence by making mistakes less costly.
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Intelligence Creation
This video segment introduces common ways to organize intelligence so that multiple people can contribute over the lifetime of an Intelligent System.
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Approaching Your Intelligent System
This video segment reviews the content, provides a checklist for approaching your Intelligent Systems project and tells you where to get more information on building intelligent systems.
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The Objective
This video segment discusses the types of problems that benefit from Intelligent Systems and gives an example of how to start simple, but achieve more and more impact over time.
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The Implementation
This video segment gives an overview of the components you can build to support your Intelligent System by allowing it to run more efficiently and adapt quickly and cheaply.
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Intelligence Orchestration
This video segment discusses the lifecycle of an Intelligent System and many of the reasons it might need to change – including mistakes.
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Vendor Evaluation
There are over 70 RPA vendors on the market. So you need to find ways to narrow the list down and find those software applications that best fit your needs.
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Example 1 – Testing Our Model
In this segment everything comes together and we see how our neural network works.
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Architecture of a Machine Learning IDS
This segment gives an overview of how the ML-based IDS is implemented.
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Voice Commands Using Arduino and Machine Learning
This segment provides a step by step guide on how to setup hardware components and use a tensorflowLite model to recognize ‘yes’ and ‘no’ voice commands.
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Bot Development
This will be a short demo of how to create a simple bot. This will be done using the UiPath platform.
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TensorFlow Model Export Formats
Save and export your trained TensorFlow models in different file formats while discovering some basics of each format.
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Example 2 – Convolutional Model
In this segment we define and test our convolutional neural network model.
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Training a Classifier
In this segment learn how to train the ML classifier components of a ML-based IDS.
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Data
AI is becoming more important with RPA. But to be successful, you need to have a data preparation plan.
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Understanding Flask API to Deploy TF Models
Deploy basic machine learning models with Flask API.
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Example 3 – Pretrained Models
In this example coding segment we demostrate how pretrained models are used.
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An Overview of the State-of-the-Art
This segment gives an overview of the state of the art for the field of Intrusion Detection via Machine Learning. Learn about possible directions of research.
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Why Use TensorFlow Lite
Look over the various features of TensorFlow Lite and how to run machine learning models on embedded devices.