Learn how to use TensorFlow 2.0 in this full tutorial course for beginners.

This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.


Course Contents

  • ⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
  • ⌨️ Module 2: Introduction to TensorFlow (00:30:08)
  • ⌨️ Module 3: Core Learning Algorithms (01:00:00)
  • ⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
  • ⌨️ Module 5: Deep Computer Vision – Convolutional Neural Networks (03:43:10)
  • ⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
  • ⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
  • ⌨️ Module 8: Conclusion and Next Steps (06:48:24)

DevOps solutions emerged as a set of practices and solutions that combines development-oriented activities (Dev) with IT operations (Ops) in order to accelerate the development cycle while maintaining efficiency in delivery and predictable, high levels of quality.

The core principles of DevOps include an Agile approach to software development, with iterative, continuous, and collaborative cycles, combined with automation and self-service concepts.

However, the DevOps approach to machine learning (ML) and AI are limited by the fact that machine learning models differ from traditional application development in many ways.

From a recent article in Forbes:

However, DevOps approaches to machine learning (ML) and AI are limited by the fact that machine learning models differ from traditional application development in many ways. For one, ML models are highly dependent on data: training data, test data, validation data, and of course, the real-world data used in inferencing. Simply building a model and pushing it to operation is not sufficient to guarantee performance. DevOps approaches for ML also treat models as “code” which makes them somewhat blind to issues that are strictly data-based, in particular the management of training data, the need for re-training of models, and concerns of model transparency and explainability.

Andon is a methodology originally designed by Toyota in the 80’s to allow responding faster and more efficiently to issues on manufacturing lines.

Skoda is modernizing the methodology to allow operators to respond more quickly to production problems, as well as automatically notify supervisors, leveraging Azure IoT Hub and Office 365.

Stepan Bechynsky joins us on the IoT Show to show how the solution implemented at Skoda works using the “hello world” of IoT demos: a simple button that operators can press to signal problems on a piece of equipment and that integrates into business applications such as Microsoft Teams.

You can learn more about Andon at https://aka.ms/iotshow/andon

Have you ever wanted to know how to setup a Hackathon?

Dona and Sarah talks to Chris Huntingford about what the Power Platform is doing for the community and how to get EVERYONE around you involved using Hackathons.

Chris is a legend amongst the community. He has worked in the industry for over a decade with Partners and Customers alike and is now working for Microsoft as a Senior Partner Technology Architect. He is the mastermind behind the Hack 4 Good initiative and continues to work on community efforts across the world. Learn more about Chris here https://www.linkedin.com/in/chrishuntingford/

To learn more, visit: https://aka.ms/LessCodeMorePowerDocs

In Kubernetes, the API server is the central way to interact and manage the cluster.

To improve cluster security in Azure Kubernetes Server, Ruchika Gupta shows Scott Hanselman how you can restrict access to the API server to a limited set of IP address ranges.

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