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EKKo Inc., a machine learning consultancy, is working on an embedded system to enable deaf or hard of hearing people to participate in online meetings and events on their mobile devices. Your task as a data scientist is to optimize this system. Using the model optimization toolkit for TensorFlow, you’ll train a CNN model with an existing American Sign Language (ASL) dataset and convert it to TFLite format to reduce the code footprint. You’ll optimize it further using quantization, drastically reducing size and CPU consumption while maintaining model accuracy. Using batch normalization to decrease the number of training cycles, you’ll significantly speed up the CNN model’s training process. Lastly, you’ll integrate the quantization changes you’ve made by compiling and training the CNN model. When you’re finished, you’ll have a fully functional quantized CNN that can be run successfully on an embedded device.
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 skills and techniques for building a fully functional CNN that’s optimized for size, speed, and accuracy, and can be run successfully on an embedded device.
geekle is based on a wordle clone.