In this podcast we cover;

1. The characteristics and types of neural networks. 

2. Enabling computer vision with deep convolution neural networks.

3. Uses and characteristics deep learning; multi-layer neural networks.

4. Risks of using deep learning in predictive maintenance.

5. Cloud AI services such as IBM Watson and AWS. 

6. Recommendation systems using knowledge graphs; Wikidata. 

7. Transfer learning and Fine tuning with NasNet and Mobile.net.

8. AI processes running in the client side with Tensorflow.js. 

9. Open source machine learning Github Repo for .net from ml.net. 

10. Machine learning tasks such as regression, multi-class classification and clustering. 

Our guest is Czako Zoltan - Masters in Computer Vision and Artificial Intelligence. Czako Zoltan contact details:

Linkedin:

https://www.linkedin.com/in/zolt%C3%A1n-czak%C3%B3-7aa623a5/

Blog: 

http://dummyprogramming.com/?fbclid=IwAR1_zdMRtPbCpYzLo4NHSVuO184gNpyHEoD_5URSD8A-m2o1Kb3vMH6ltxY

Medium:

https://medium.com/@czakozoltan08

Useful links:

ML.net

https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet

Tensorflow.js

https://js.tensorflow.org/

NasNet

https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet?fbclid=IwAR0M-Ay_EiZ6qY04MKDrNKE8LwmzVEcpLDOZb9I9qZc1uNi2_CjrxTy7Sjw

AutoML

https://ai.googleblog.com/2017/11/automl-for-large-scale-image.html?fbclid=IwAR2yaQ1O1k2kdBlqTEh1kxJ3fVKFQCYkmlcbIWEKZh8LbZVS4HeZOMrVtQQ

Transfer Learning

https://arxiv.org/pdf/1707.07012.pdf?fbclid=IwAR0mR0vhTgEjqnnAtsKi3ZndOzgPld9Zjg3YEZFy6xZ08t8nKBE0rg9HiZA