Embedded Systems

Train a CNN you own this product

This project is part of the liveProject series Machine Learning on Embedded Systems
prerequisites
intermediate Python • basic ML • basic edge computing systems • intermediate TensorFlow
skills learned
preprocess the data • create and configure a CNN Model • use regularization to prevent overfitting • use learning rate (LR) to speed up model training• model training and introducing Callbacks
Kanishka Tyagi & Raghavendra Sriram
1 week · 6-8 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Machine Learning on Embedded Systems liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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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.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project authors

Kanishka Tyagi

Dr. Kanishka Tyagi received his bachelor's degree in electrical engineering in 2008 from Pantnagar, India. Later he worked as a research associate at the Department of Electrical Engineering, Indian Institute of Technology, Kanpur, with Dr. P.K.Kalra. He received his master’s and doctoral degree with Dr. Michael Manry in the Department of Electrical Engineering at The University of Texas at Arlington in 2012 and 2017. Currently, he works as a senior machine learning scientist at Aptiv advance research center, California. Prior to Aptiv, he worked at Siemens research, and interned in machine learning groups at The MathWorks and Google Research. He has worked as a visiting researcher at Ajou University and Seoul National University. He received the 2007 and 2011 IEEE CIS Outstanding Student Paper Travel Grant Award and 2013 IEEE CIS Walter Karplus Summer Research Grant award. Dr. Tyagi is an IEEE senior member and member of various IEEE-CIS committees. He currently serves as an associate editor for IEEE Transaction on Neural Network and Learning Systems. Dr. Tyagi has published over 30 papers and filed 17 U.S. patents and trade secrets.

Raghavendra Sriram

Raghavendra Sriram completed his bachelor’s degree in engineering at the Department of Electrical and Electronics Engineering at Canara Engineering College in Mangalore in 2012. Currently, he’s a senior development engineer for OBD systems working at Paccar, Inc. in Mt. Vernon, WA. His work focuses on developing innovative solutions to diagnostic capabilities for diesel engine misfire detection and calibration efforts using machine learning and optimization techniques. Previously, he developed and designed diagnostic algorithms for after-treatment systems. Before joining the industry, he worked as a researcher under the guidance of Dr. Frank L. Lewis in the electrical department at the University of Texas at Arlington, focusing mainly on developing and implementing intelligent control algorithms on various robotics platforms. He has extensive experience with multiple rapid prototyping systems and tools, has been awarded several university department scholarships for research, and contributed to several journals and research groups.

prerequisites

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
  • Raspberry Pi/edge computer
  • Ubuntu Desktop 18.04 +
  • Intermediate Python
  • Basics of Jupyter Notebook
  • Intermediate NumPy
  • Intermediate TensorFlow
  • Conda/pip virtual environment setup skills
TECHNIQUES
  • Basic data science
  • Understand logistic regressions and classification
  • Load data on a neural network
  • Train a neural network
  • Assess a CNN model
  • Basics of linear algebra (vectors, spaces, matrix transformations)

you will learn

In this liveProject, you’ll learn to use TensorFlow tools to configure various CNN hyperparameters, train a CNN onto an embedded board, and generate predictions from the CNN.

  • Basic machine learning concepts
  • Basics of edge computing systems like Raspberry Pi and hardware integration
  • Techniques to understand preprocessing of datasets, ML model development, training, optimization, deployment, and validation techniques

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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