Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)

Microsoft Cognitive Toolkit (CNTK)    |    Expert
  • 15 Videos | 53m 40s
  • Includes Assessment
  • Earns a Badge
Likes 4 Likes 4
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

  • 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

  • Playable
    1. 
    Course Overview
    2m 26s
    UP NEXT
  • Playable
    2. 
    CNTK vs. Other Platforms
    4m
  • Locked
    3. 
    Working With Data in CNTK
    2m 38s
  • Locked
    4. 
    CNTK Training Using Imperative APIs
    2m 29s
  • Locked
    5. 
    CNTK Training Using Declarative APIs
    2m 45s
  • Locked
    6. 
    Epochs and Batch Sizes in CNTK
    3m 47s
  • Locked
    7. 
    Model Serialization Using CNTK
    2m 59s
  • Locked
    8. 
    Model Visualization Using CNTK
    3m 35s
  • Locked
    9. 
    CNTK Model Training
    3m 8s
  • Locked
    10. 
    CNTK Model Evaluation
    3m 8s
  • Locked
    11. 
    Diabetes Prediction Using CNTK
    3m 31s
  • Locked
    12. 
    Credit Rating Prediction Using CNTK
    3m 45s
  • Locked
    13. 
    Housing Price Prediction Using CNTK
    4m 11s
  • Locked
    14. 
    Salary Prediction Using CNTK
    3m 53s
  • Locked
    15. 
    Course Summary
    56s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Likes 3 Likes 3  
Likes 11 Likes 11