Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)
Microsoft Cognitive Toolkit (CNTK)
| Expert
- 15 Videos | 47m 10s
- Includes Assessment
- Earns a Badge
Microsoft Cognitive Toolkit provides powerful machine learning and deep learning algorithms for developing AI. Knowing which problems are easier to solve using Microsoft CNTK over other frameworks helps AI practitioners decide on the best software stack for a given application. In this course, you'll explore advanced techniques for working with Microsoft CNTK and identify which cases benefit most from MS CNTK. You'll examine how to load and use external data using CNTK and how to use its imperative and declarative APIs. You'll recognize how to carry out common AI development tasks using CNTK, such as working with epochs and batch sizes, model serialization, model visualization, feedforward neural networks, and machine learning model evaluation. Finally, you'll implement a series of practical AI projects using Python and MS CNTK.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
specify cases in which it's advantageous to use CNTK over other platforms
describe how to load and use external data using Microsoft CNTK
outline the CNTK training process when called with an imperative API
outline the CNTK training process when called with a declarative API
define epochs and batch sizes in CNTK and specify how to choose the optimal values for best performance
recognize the model serialization process using CNTK
identify how CNTK can be used for model visualization -
use CNTK to create and train a feedforward neural network and demonstrate its performance
work with CNTK evaluation tools to evaluate previously created CNTK machine learning models
use Python to apply pre-processing techniques to diabetic patients' data and use this data to troubleshoot the creation and training of CNTK machine learning classification models
use Python to apply pre-processing techniques to credit rating data and use this data to troubleshoot the creation and training of CNTK machine learning regression models
utilize Python to apply pre-processing techniques to housing price data and use this data to troubleshoot the creation and training of CNTK machine learning regression models
" utilize Python to apply pre-processing techniques to professional salary data and use this data to troubleshoot the creation and training of CNTK machine learning classification models "
summarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 26sUP NEXT
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2.CNTK vs. Other Platforms4m
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3.Working With Data in CNTK2m 38s
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4.CNTK Training Using Imperative APIs2m 29s
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5.CNTK Training Using Declarative APIs2m 45s
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6.Epochs and Batch Sizes in CNTK3m 47s
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7.Model Serialization Using CNTK2m 59s
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8.Model Visualization Using CNTK3m 35s
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9.CNTK Model Training3m 8s
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10.CNTK Model Evaluation3m 8s
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11.Diabetes Prediction Using CNTK3m 31s
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12.Credit Rating Prediction Using CNTK3m 45s
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13.Housing Price Prediction Using CNTK4m 11s
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14.Salary Prediction Using CNTK3m 53s
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15.Course Summary56s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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