Idiomatic Programmer - Learning Keras

I am finding UI/UX save bugs previously solved in Fedwiki have come back (to haunt). @Ward @Paul. i.e. Alt+S no longer saves on ChromeOS instead Wiki has reverted to launch the Chrome file manager with any save. A painful but liveable UI/UX bug.

.

Andrew Ferlitsch is really good idiomatic explainer.

Abstract: As AI/ML become more accessible to non-researchers, tech companies are now looking for experienced engineers or data scientists who can apply machine learning principles to production grade software. They can understand the core principles, best practices, design patterns, and expertise with a ML framework/toolset.

Abstract: As AI/ML become more accessible to non-researchers, tech companies are now looking for experienced engineers or data scientists who can apply machine learning principles to production grade software. They can understand the core principles, best practices, design patterns, and expertise with a ML framework/toolset. Take-Away: For the attendees that have previously taken a ML course or self-taught, but don’t feel they are ready for a production environment, this course purpose is to push your over that final hurdle. Session 1: Computer Vision Models Overview of CNN Sequential CNN Residual Network CNN Wide Layer CNN Densely Connected CNN Stem Groups Representational Equivalence Session 2: Computer Vision Data Engineering Data Curation Data Collection Data Preprocessing (openCV and Keras) Data Augmentation (openCV and Keras) Session 3: Computer Vision Training and Deployment Hyperparameters Training Deployment Hyperparameter Search Prebuilt Models and Transfer Learning Wrap up and next steps: On-device ML - how to deploy your trained model to mobile and IoT.

Take-Away: For the attendees that have previously taken a ML course or self-taught, but don’t feel they are ready for a production environment, this course purpose is to push your over that final hurdle. Session 1: Computer Vision Models Overview of CNN Sequential CNN Residual Network CNN Wide Layer CNN Densely Connected CNN Stem Groups Representational Equivalence Session 2: Computer Vision Data Engineering Data Curation Data Collection Data Preprocessing (openCV and Keras) Data Augmentation (openCV and Keras) Session 3: Computer Vision Training and Deployment Hyperparameters Training Deployment Hyperparameter Search Prebuilt Models and Transfer Learning

Wrap up and next steps: On-device ML - how to deploy your trained model to mobile and IoT. handbooks