Deep Learning on ARM Processors - From Ground Up™ Udemy Free Download
What you'll learn:
- Build Neural Networks from scratch without libraries
- Master quantization methods for deploying Neural Networks on microcontrollers
- Build a Deep Learning Firmware for Image Classification
- Build a Deep Learning firmware for Human Activity Recognition (HAR) e.g. running,walking etc.
- Build a Deep Learning Firmware for Handwriting Recognition
- Build a Deep Learning Firmware for Acoustic Scene Classification (ASC)
- Be able to give a Lecture on Deep Learning
Requirements::
- STM32F411-NUCLEO BOARD
- STM32F429 -DISCO BOARD
Description:
Arm copyright material reproduced by kind permission of Arm Limited. Copyright © Arm Limited. All Arm trademarks featured in this course are registered or unregistered trademarks of Arm Limited (or its subsidiaries) in the US or elsewhere. All rights reserved.
Welcome to the Deep Learning From Ground Up™ on ARM Processors course.
We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch on our microcontrollers.
We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network. All of this on our microcontroller, both training and inference.
As we begin to deal with large datasets we shall start training our neural networks on our computers and then deploying the the trained models on our microcontrollers. Due to the limited memory and processing power of microcontrollers we shall learn methods for quantizing our models before deploying them on our resource constrained microcontrollers without compromising the accuracy of our models.
We shall also learn how to thoroughly take advantage of deep learning libraries such as Keras and Tensorflow as well as microncontroller specific deep learning libraries such as CMSIS-NN, CubeMX.AI and TensorFlow Lite.
By the end of this course you will be able to build neural networks from scratch without libraries, be able to master quantization methods for deploying neural networks on microcontrollers , be able to build deep learning firmware for Image Classification, be able to build deep learning firmware for Human Activity Recognition, be able to build deep learning firmware for Handwriting Recognition, be able to use Keras, CMSIS-NN, CubeMX.AI and so much more.
If you are new to machine learning and deep learning, this course is for you. The course starts from the very basic building block of neural networks and teaches you how to build your own neural network using pure c code before we move on to see how to use readily available libraries.
If you already have some experience with deep learning and want to see how to deploy models on microcontrollers you can also join this course. This course gives an in-depth training on the design methodology that needs to be adopted in order to be to deploy models on resource constrained microcontrollers.
Sign up and lets start building some intelligent firmware.
Who this course is for:
- If you are new to machine learning and deep learning, this course is for you. The course starts from the very basic building block of neural network and teaches you how to build your own neural network using pure c code before we move on to see how to use readily available libraries.
- If you already have some experience with deep learning and want to see how to deploy models on microcontrollers you can also join this course. The course gives in-depth train on the design methodology that needs to be adopted in order to be to deploy models on microcontrollers.