You’re a data scientist at EKKo Inc., a machine learning consultancy that has won a contract to create an embedded system that can be implemented on embedded systems. This system will enable people who are deaf or hard of hearing to participate in online meetings and events. After setting up an environment for running ML programs on an embedded system, you’ll process data from the existing American Sign Language (ASL) dataset and build, train, validate, and analyze the performance of a logistic regression model. By the end of this liveProject, you’ll be able to install, configure, and deploy a basic logistic regression algorithm that’s capable of classifying the letters of the ASL image dataset.

You’re a data scientist at EKKo Inc., a machine learning consultancy that’s working on an embedded system to help deaf or hard of hearing people participate in online meetings and events on their mobile devices. To help the system recognize the hand gestures that represent the letters in American Sign Language (ASL), your task is to classify them using a convolutional neural network (CNN), an algorithm widely used for image processing applications.

You’ll write a Python script that preprocesses the ASL dataset, ensuring the model can interpret it. Using TensorFlow, a popular choice for such tasks, you’ll create and configure the CNN model. You’ll train the model and improve its performance by adding regularization to avoid overfitting, fine-tuning the learning rate (LR) to increase training speed, and introducing callbacks to monitor the training process. When you’re done, you’ll have firsthand experience using TensorFlow tools to configure various CNN hyperparameters, train a CNN onto an embedded board, and generate predictions from the CNN.

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.

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.