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EKKo Inc., the machine learning consultancy you work for, has been building an embedded system to enable deaf or hard of hearing people to participate in online meetings and events on their mobile devices. As a data scientist, your task is to complete the system by enabling it to detect American Sign Language (ASL) in real time. You’ll define a CNN model, train it using the existing ASL dataset, and evaluate the model’s accuracy. To optimize training speed and quality, you’ll fine-tune the model’s hyperparameters using the Keras Tuner. You’ll enable the model to be quantization-aware with the TensorFlow Model Optimization Toolkit (MoT), optimizing size and CPU consumption while maintaining model accuracy. To complete the project, you’ll connect the quantized model with a live video stream and train it on the embedded system. When you’re done, you’ll have a fully functional quantized CNN you can run on an embedded system that successfully detects and transcribes ASL in real time.
The liveProject is for intermediate Python programmers who know the basics of data science. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn to design and build a real-time implementation of a CNN model that’s optimized for an embedded platform.
geekle is based on a wordle clone.