Deep Learning on ARM Processors – From Ground Up

Deep Learning on ARM Processors From Ground Up


Deep Learning on ARM Processors – From Ground Up is the name of a deep learning course using ARM processors. In this course we will go on an interesting journey together where we will learn how to build deep neural networks from the basics of working on microcontrollers. This course will start by learning the basics of deep learning with practical codes that will show all the basic building blocks that lead to the construction of a huge neural network. All of these, both training and inference, are done on our microcontrollers. In this course, the useful use of famous libraries such as Keras and Tensorflow will be taught along with famous deep learning libraries for microcontrollers such as CMSIS-NN, CubeMX.AI and TensorFlow Lite.

What you will learn in Deep Learning on ARM Processors – From Ground Up course:

  • Building neural networks from scratch without using libraries
  • Master quantization methods to expand neural networks on microcontrollers
  • Building a framework for recognizing human activity (Human Activity Recognition – HAR) such as walking, running, etc.
  • Building a deep learning framework for handwriting recognition
  • Building a deep learning framework for Acoustic Scene Classification (ASC)
  • You will be able to give a speech about deep learning.

Course details:

Publisher: Udemy
teacher: Israel Gbati And EmbeddedExpertIO
English language
Training level: introductory to advanced
Number of courses: 118
Duration: 19 hours and 16 minutes

Course headings

1. Introduction
2. Building Blocks of Neural Networks
3. Introduction to Neural Network (Part 2)
4. Logistic Regression
5. Deep Neural Networks
6. Improving Neural Networks with Regularization Techniques
7. Building A Logistic Regression Model
8. Building Deep Neural Networks From Scratch
9. Convolutional Neural Networks (CNN)
10. CubeMX 5 & CubeIDE Primer
11. CubeMX AI
12. Case Study Deploying the MNIST Handwriting Recognition Model on ARM MCUs
13. Setup
14. Python Essentials
15. CubeMX Primer
16. Closing

Course prerequisites:



Sample video

Installation guide

After Extract, view with your favorite Player.

English subtitle

Quality: 720

download link

Download part 1 – 2 GB

Download part 2 – 2 GB

Download part 3 – 2 GB

Download part 4 – 2 GB

Download part 5 – 1.23 GB

Password file(s):


9.2 GB

4.4/5 – (3983 points)

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