[LYNDA] Machine Learning in Mobile Applications [FCO] GloDLS
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Torrent File Contents
[LYNDA] Machine Learning in Mobile Applications [FCO] GloDLS
1.Introduction/01.Machine learning in mobile apps.mp4
1.Introduction/02.What you should know.mp4
1.Introduction/03.Using the exercise files.mp4
2.1. Introduction to Machine Learning/04.What is machine learning.mp4
2.1. Introduction to Machine Learning/05.Required concepts.mp4
2.1. Introduction to Machine Learning/06.Why does this matter for my app.mp4
2.1. Introduction to Machine Learning/07.Training a model.mp4
2.1. Introduction to Machine Learning/08.Machine learning vs. deep learning.mp4
2.1. Introduction to Machine Learning/09.What can I do with machine learning.mp4
2.1. Introduction to Machine Learning/10.Server-side vs. client-side ML.mp4
2.1. Introduction to Machine Learning/11.ML frameworks.mp4
3.2. Server Models - IBM Watson/12.Overview of Watson.mp4
3.2. Server Models - IBM Watson/13.Natural Language Understanding - Set up.mp4
3.2. Server Models - IBM Watson/14.Natural Language Understanding - Train the model.mp4
3.2. Server Models - IBM Watson/15.Visual Recognition - Set up.mp4
3.2. Server Models - IBM Watson/16.Visual Recognition - Train the model.mp4
3.2. Server Models - IBM Watson/17.Create a custom model.mp4
3.2. Server Models - IBM Watson/18.Train and deploy a custom model.mp4
3.2. Server Models - IBM Watson/19.Install client SDK package.mp4
3.2. Server Models - IBM Watson/20.Client tie to Natural Language.mp4
3.2. Server Models - IBM Watson/21.Client tie to Visual Recognition call setup.mp4
3.2. Server Models - IBM Watson/22.Client tie to Visual Recognition response.mp4
3.2. Server Models - IBM Watson/23.Client tie to custom model - Get an access token.mp4
3.2. Server Models - IBM Watson/24.Client tie to call custom model service.mp4
3.2. Server Models - IBM Watson/25.Client tie to get custom model response.mp4
3.2. Server Models - IBM Watson/26.Run the client app.mp4
4.3. Server Models - Azure Machine Learning/27.Azure Machine Learning overview.mp4
4.3. Server Models - Azure Machine Learning/28.Language Understanding - Set up.mp4
4.3. Server Models - Azure Machine Learning/29.Language Understanding - Intents.mp4
4.3. Server Models - Azure Machine Learning/30.Language Understanding - Utterances.mp4
4.3. Server Models - Azure Machine Learning/31.Custom Vision - Set up.mp4
4.3. Server Models - Azure Machine Learning/32.Machine Learning Studio - Set up.mp4
4.3. Server Models - Azure Machine Learning/33.Machine Learning Studio - Create model.mp4
4.3. Server Models - Azure Machine Learning/34.Machine Learning Studio - Publish model.mp4
4.3. Server Models - Azure Machine Learning/35.Install client SDK package.mp4
4.3. Server Models - Azure Machine Learning/36.Client tie to LUIS.mp4
4.3. Server Models - Azure Machine Learning/37.Client tie to Custom Vision model.mp4
4.3. Server Models - Azure Machine Learning/38.Client tie to custom model.mp4
4.3. Server Models - Azure Machine Learning/39.Client tie to custom model - Set up request.mp4
4.3. Server Models - Azure Machine Learning/40.Client tie to custom model - Make the call.mp4
4.3. Server Models - Azure Machine Learning/41.Run the clent app.mp4
5.4. Client Models - Core ML/42.Core ML overview.mp4
5.4. Client Models - Core ML/43.Core ML - Create Natural Language model.mp4
5.4. Client Models - Core ML/44.Core ML - Create Visual Recognition model.mp4
5.4. Client Models - Core ML/45.Client tie to Natural Language model.mp4
5.4. Client Models - Core ML/46.Client tie to Visual Recognition model.mp4
5.4. Client Models - Core ML/47.Client tie to Visual Recognition - Converting model.mp4
5.4. Client Models - Core ML/48.Run the client app.mp4
6.5. Understanding the Offerings/49.Different philosopies of the vendors.mp4
6.5. Understanding the Offerings/50.Why client-side model vs. server-side.mp4
6.5. Understanding the Offerings/51.When to use one or the other of these solutions.mp4
7.Conclusion/52.Next steps.mp4
Exercise Files/Ex_Files_Machine_Learning_Mobile_App.zip
Discuss.FTUForum.com.html
FreeCoursesOnline.Me.html
FTUForum.com.html
How you can help Team-FTU.txt
[TGx]Downloaded from torrentgalaxy.org.txt
Torrent Downloaded From GloDls.buzz.txt
1.Introduction/01.Machine learning in mobile apps.mp4
- 10.49 MB
1.Introduction/02.What you should know.mp4
- 5.55 MB
1.Introduction/03.Using the exercise files.mp4
- 12.89 MB
2.1. Introduction to Machine Learning/04.What is machine learning.mp4
- 5.87 MB
2.1. Introduction to Machine Learning/05.Required concepts.mp4
- 7.32 MB
2.1. Introduction to Machine Learning/06.Why does this matter for my app.mp4
- 7.39 MB
2.1. Introduction to Machine Learning/07.Training a model.mp4
- 3.74 MB
2.1. Introduction to Machine Learning/08.Machine learning vs. deep learning.mp4
- 4.14 MB
2.1. Introduction to Machine Learning/09.What can I do with machine learning.mp4
- 4.41 MB
2.1. Introduction to Machine Learning/10.Server-side vs. client-side ML.mp4
- 4.64 MB
2.1. Introduction to Machine Learning/11.ML frameworks.mp4
- 5.51 MB
3.2. Server Models - IBM Watson/12.Overview of Watson.mp4
- 4.26 MB
3.2. Server Models - IBM Watson/13.Natural Language Understanding - Set up.mp4
- 6.48 MB
3.2. Server Models - IBM Watson/14.Natural Language Understanding - Train the model.mp4
- 12.31 MB
3.2. Server Models - IBM Watson/15.Visual Recognition - Set up.mp4
- 4.48 MB
3.2. Server Models - IBM Watson/16.Visual Recognition - Train the model.mp4
- 11.97 MB
3.2. Server Models - IBM Watson/17.Create a custom model.mp4
- 12.73 MB
3.2. Server Models - IBM Watson/18.Train and deploy a custom model.mp4
- 10.08 MB
3.2. Server Models - IBM Watson/19.Install client SDK package.mp4
- 7.62 MB
3.2. Server Models - IBM Watson/20.Client tie to Natural Language.mp4
- 21.62 MB
3.2. Server Models - IBM Watson/21.Client tie to Visual Recognition call setup.mp4
- 22 MB
3.2. Server Models - IBM Watson/22.Client tie to Visual Recognition response.mp4
- 20.07 MB
3.2. Server Models - IBM Watson/23.Client tie to custom model - Get an access token.mp4
- 21.03 MB
3.2. Server Models - IBM Watson/24.Client tie to call custom model service.mp4
- 30.34 MB
3.2. Server Models - IBM Watson/25.Client tie to get custom model response.mp4
- 10.45 MB
3.2. Server Models - IBM Watson/26.Run the client app.mp4
- 9.51 MB
4.3. Server Models - Azure Machine Learning/27.Azure Machine Learning overview.mp4
- 4.52 MB
4.3. Server Models - Azure Machine Learning/28.Language Understanding - Set up.mp4
- 6.54 MB
4.3. Server Models - Azure Machine Learning/29.Language Understanding - Intents.mp4
- 10.09 MB
4.3. Server Models - Azure Machine Learning/30.Language Understanding - Utterances.mp4
- 9.83 MB
4.3. Server Models - Azure Machine Learning/31.Custom Vision - Set up.mp4
- 12.61 MB
4.3. Server Models - Azure Machine Learning/32.Machine Learning Studio - Set up.mp4
- 12.34 MB
4.3. Server Models - Azure Machine Learning/33.Machine Learning Studio - Create model.mp4
- 9.15 MB
4.3. Server Models - Azure Machine Learning/34.Machine Learning Studio - Publish model.mp4
- 8.31 MB
4.3. Server Models - Azure Machine Learning/35.Install client SDK package.mp4
- 5.89 MB
4.3. Server Models - Azure Machine Learning/36.Client tie to LUIS.mp4
- 17.8 MB
4.3. Server Models - Azure Machine Learning/37.Client tie to Custom Vision model.mp4
- 17.5 MB
4.3. Server Models - Azure Machine Learning/38.Client tie to custom model.mp4
- 13.3 MB
4.3. Server Models - Azure Machine Learning/39.Client tie to custom model - Set up request.mp4
- 22.56 MB
4.3. Server Models - Azure Machine Learning/40.Client tie to custom model - Make the call.mp4
- 29.65 MB
4.3. Server Models - Azure Machine Learning/41.Run the clent app.mp4
- 5.94 MB
5.4. Client Models - Core ML/42.Core ML overview.mp4
- 3.91 MB
5.4. Client Models - Core ML/43.Core ML - Create Natural Language model.mp4
- 15.4 MB
5.4. Client Models - Core ML/44.Core ML - Create Visual Recognition model.mp4
- 8.36 MB
5.4. Client Models - Core ML/45.Client tie to Natural Language model.mp4
- 17.3 MB
5.4. Client Models - Core ML/46.Client tie to Visual Recognition model.mp4
- 13.45 MB
5.4. Client Models - Core ML/47.Client tie to Visual Recognition - Converting model.mp4
- 8.28 MB
5.4. Client Models - Core ML/48.Run the client app.mp4
- 4.76 MB
6.5. Understanding the Offerings/49.Different philosopies of the vendors.mp4
- 6.12 MB
6.5. Understanding the Offerings/50.Why client-side model vs. server-side.mp4
- 6.4 MB
6.5. Understanding the Offerings/51.When to use one or the other of these solutions.mp4
- 10.56 MB
7.Conclusion/52.Next steps.mp4
- 10.15 MB
Exercise Files/Ex_Files_Machine_Learning_Mobile_App.zip
- 131.94 MB
Discuss.FTUForum.com.html
- 31.89 KB
FreeCoursesOnline.Me.html
- 108.3 KB
FTUForum.com.html
- 100.44 KB
How you can help Team-FTU.txt
- 235 Bytes
[TGx]Downloaded from torrentgalaxy.org.txt
- 524 Bytes
Torrent Downloaded From GloDls.buzz.txt
- 84 Bytes
Torrent Description
Description:
Author: Kevin Ford
Last updated: 3/21/2019
Language: English
Torrent Contains: 59 Files, 8 Folders
Course Source: https://www.lynda.com/course-tutorials/Machine-Learning-Mobile-Applications/791354-2.html
Description:
Machine learning is reaching the mainstream. With the new tools available to developers, it's now possible to implement machine learning features—voice, face, and image recognition; personalized recommendations; and more—in a mobile context. This course explores how to apply the power of machine learning to mobile app development, using platforms such as IBM Watson, Microsoft Azure Cognitive Services, and Apple Core ML. Instructor Kevin Ford demos each product, reviewing the different features and approaches to machine learning. He shows how to train and deploy models for natural language and visual recognition and how to generate statistical models for use in a Xamarin application. In chapter five, he compares client-side and server-side models and explains when a developer might choose one platform over another.
Topics include:
• Defining machine learning
• Training a machine learning model
• Comparing machine learning frameworks
• Using IBM Watson for mobile machine learning
• Using Azure Machine Learning for speech and image recognition
• Training Core ML models
• Comparing client-side and server-side models.
Author: Kevin Ford
Last updated: 3/21/2019
Language: English
Torrent Contains: 59 Files, 8 Folders
Course Source: https://www.lynda.com/course-tutorials/Machine-Learning-Mobile-Applications/791354-2.html
Description:
Machine learning is reaching the mainstream. With the new tools available to developers, it's now possible to implement machine learning features—voice, face, and image recognition; personalized recommendations; and more—in a mobile context. This course explores how to apply the power of machine learning to mobile app development, using platforms such as IBM Watson, Microsoft Azure Cognitive Services, and Apple Core ML. Instructor Kevin Ford demos each product, reviewing the different features and approaches to machine learning. He shows how to train and deploy models for natural language and visual recognition and how to generate statistical models for use in a Xamarin application. In chapter five, he compares client-side and server-side models and explains when a developer might choose one platform over another.
Topics include:
• Defining machine learning
• Training a machine learning model
• Comparing machine learning frameworks
• Using IBM Watson for mobile machine learning
• Using Azure Machine Learning for speech and image recognition
• Training Core ML models
• Comparing client-side and server-side models.