Automated testing by a developer is still a topic that is heavily contested. Developers continue to struggle to understand how to test their apps, when to test, and how to put this all together into a continuous integration pipeline. This process can be surprisingly simple with the combination of the right tools. In this live-coding session developers will learn how to create a web-app using React. Afterward, we will shift-left by adding some automated unit tests using Jest. Next, we will shift-right by adding some functional tests using Cypress.io. Finally, the developers will learn how to continuously build and test the web app through a Github workflow. By the end of the session, the developers will: – Learn a bit about unit testing with Jest – Learn Cypress for functional testing – Put all of this together into a CI pipeline that builds and tests in a continuous manner. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=bJBDsLOpHHs https://www.youtube.com/watch?v=13HIqdtfxrM

The lecturer discusses the motivation behind deep learning. He begins with the history and inspiration of deep learning. Then he discusses the history of pattern recognition and introduces gradient descent and its computation by backpropagation. Finally, he discusses the hierarchical representation of the visual cortex. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=0bMe_vCZo30&t=3s https://www.youtube.com/watch?v=19Vy5Yi6fNI

Check out Carl Osipov's book Serverless Machine Learning in Action | http://mng.bz/YrEj
📚📚📚 To save 40% on this book use the Discount Code: twitosip40 📚📚📚 Deep dive into understanding automatic differentiation used by PyTorch autograd for deep learning with the help of Carl Osipov, who has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. "Serverless Machine Learning in Action: With PyTorch on AWS" is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You’ll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you’ll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. When you’re done, you’ll have the tools to easily bridge the gap between ML models and a fully functioning production system. https://www.youtube.com/watch?v=9aBpF0-JH7A