Embedded Systems

Train a Logistic Regression you own this product

This free project is part of the liveProject series Machine Learning on Embedded Systems
prerequisites
intermediate Python • basic machine learning • basic edge computing systems • intermediate TensorFlow
skills learned
hardware and software environment setup • basics of how neural networks function • data preprocessing and splitting for training, validation, and testing
Kanishka Tyagi & Raghavendra Sriram
1 week · 6-8 hours per week · INTERMEDIATE
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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.

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/NVIDIA Jetson
  • Ubuntu Desktop 18.04 +
  • Intermediate Python (importing modules, plotting using Matplotlib)
  • Basics Jupyter Notebook
  • Intermediate NumPy
  • Intermediate TensorFlow
  • Conda/pip virtual environment setup skills
TECHNIQUES
  • Basic data science
  • Understand logistic regressions and classification
  • Neural network loading data, training, neural network model assessment
  • Basic linear algebra (vectors and spaces, and matrix transformations)

you will learn

In this liveProject, you’ll learn how to develop, train and deploy machine learning algorithms and projects on edge systems.

  • Basics of edge computing systems like Raspberry Pi, and hardware integration
  • Develop, train, optimize, deploy, and validate machine learning models
  • Basics of how neural networks function

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