Embedded Electronics Bootcamp: From Bit to Deep Learning

[mitsumi]

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 8.98 GB | Duration: 16h 18m
Embedded Systems, IoT, RTOS, Deep Learning, Linux and Raspberry PI, ESP32, Arduino

What you'll learn
Hardware Design Using FPGA by Learning VHDL
Raspberry Pi, Arduino and ESP32
Microcontroller Programming and Simulation
Multi-Threading For Embedded Systems and RTOS
IoT, Remote Control and Monitoring for Embedded Systems
Linux Based Embedded Systems
Python
Deep Learning and Image Processing

Description
Hardware Technologies to be taught:

FPGA

Raspberry PI

Arduino

ESP32 (Node MCU)

Programming Languages to be taught:

C

Python

VHDL

Communication and Cloud Technologies to be taught:

UART

SPI

MQTT

Node-Red

Hivemq

Techniques to be taught:

Combinational Logic Design

Sequential Logic Design

FSM

Control Units

Tinker CAD

Digital and Analog Signals

Interrupts

Android Control

Remote Control

RTOS

Semaphores

Mutexes

Sharing Resources

Queues

Parametrized Tasks

Structures

Linux

Basics of Artificial Intelligence

Neural Networks

Deep Neural Networks

CNN

No other E-learning content tried linking all digital sciences with embedded systems like we did.

Starting with FPGA and the VHDL hardware design programming language. specifically from the smallest signal which we call the bit, to building simplified calculation and registration units used in microcontrollers from scratch! And what I mean by scratch is, building it out of basic Logic gates and registers.

Then Moving to AVR uC and the famous Arduino, and building it up to run The famous Realtime operating system (RTOS) in order to run Multi-threading based application.

Then dialing it up a notch and introducing ESP boards to run IoT applications, establishing communication to Node-red, android devices and learning about remote access control.

Closing the whole thing by introducing raspberry pi and Linux. And building up with a basic Python introduction, Neural Networks, before building Embedded Deep learning image processing based models.

And all of that is hands on! No plain theory, no philosophical block of texts explaining useless concepts. Getting your hands dirty, is the my main drive here.

Who this course is for:
Anyone who want to learn about Embedded Systems from Scratch
Electronics Hobbyists
Robotics Hobbyists
Computer Engineers
Electrical and Electronics Engineers

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