Artificial Intelligence Image Processing

Hands-On “Deep Learning” Videos: Now on YouTube

Every new product or service claims to use deep learning or neural networks. But: how do they really work? What can machine learning do? How complicated is it to get started?

In the 4-part video series “Deep Learning Hands-On with TensorFlow 2 & Python”, you’ll learn what many of the buzzwords are about and how they relate to the problems you want to solve.

By watching the short videos, your journey will start with the background of neural networks, which are the base of deep learning. Then, two practical examples show two concrete applications on how you can use neural networks to perform classification with TensorFlow:

  • Breast cancer classification: based on numerical / categorical data
  • Hand-written image classification: the classic MNIST dataset based on small images

In the last part, we’ll look at one of the most important specialized variants of neural networks: convolutional neural networks (CNNs), which are especially well-suited for image classification.

Watching all four videos gives you a thorough understanding of how deep learning works and the guidance to get started!

AR / VR HoloLens Image Processing

Basics of AR: Anchors, Keypoints & Feature Detection

Creating apps that work well with Augmented Reality requires some background knowledge of the image processing algorithms that work behind the scenes. One of the most fundamental concepts involves anchors. These rely on keypoints and their descriptors, detected in the recording of the real world.

Anchor Virtual Objects to the Real World

AR development APIs hide much of the complexity. As a developer, you simply anchor virtual objects to the world. This ensures that the hologram stays glued to the physical location where you put it.

App Development Smart Home

Using Netgear Arlo Security-Cameras for Periodic Recording

The Arlo security camera by Netgear is one of the few cameras that doesn’t need a power supply, so is easier to use outdoors. The cameras have motion-sensing integrated and upload a short video sequence around the motion event to the Netgear backend. Great about the Arlo ecosystem is that this is possible with the free plan as well; you can access the recordings of the last 7 days already with the free basic plan.

For my use case, I wanted to also take periodic pictures / recordings. These can then later be stitched together for a time-lapse.