Setup TensorFlow with GPU acceleration using Windows, Docker and WSL2

“Docker is the easiest way to run TensorFlow on a GPU…”

One of the fastest ways to get started with TensorFlow and Keras on your local developer machine is using Docker. This is especially useful if you are working on a Windows machine with an NVIDIA GPU to speed up model training and inference. I have put together the following detailed setup guide to help you get up and running more quickly.

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When Your Smart Doorbell or Camera Isn’t Enough, Try a Raspberry Pi

CamKnows™ 1.0 for the Raspberry Pi is now available on GitHub.

I have owned a smart doorbell for several years now, and I can’t imagine life without it. The benefits of smart cameras and doorbells are numerous. I can instantly know who is at my front door or when a package is delivered. There are, however, some drawbacks to any camera-equipped smart device.

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Caching Deep Learning Models with TensorFlow, Keras and PTMLib

Save Machine Learning models, time, and energy (and maybe even the planet)

I’ve noticed when going through lengthy ML tutorials for Keras and TensorFlow that I often run the same models repeatedly in Jupyter Notebook, regardless of the fact they have no changes. This is natural for me with a notebook with multiple models. I also find that typing the code into a new notebook, rather than just clicking Shift + Enter, aids my understanding and retention of what I have learned.

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How to Install OpenCV for Python on a Raspberry Pi

Computer Vision isn’t just for GPU-powered desktops. Using OpenCV and Python on a Raspberry Pi equipped with a Camera Module, you can program and deploy amazing solutions including motion detection.

As I discovered personally, installing the correct libraries for OpenCV and Python for this task can be challenging. Here are the steps I used to get it working.

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Xandly5 – Generate Lyrics with Deep Learning and Natural Language Processing

Xandly5 is a lyrics generator powered by Natural Language Processing (NLP) and the Keras and TensorFlow frameworks. Deep Learning models are trained on separate collections…

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