📝 STM32 Tutorials, “ARM-Based Microcontrollers Programming For Embedded Systems Enthusiasts”
Completely free course for those who want to start learning how to work with the most popular development board, improve their programming skills or refresh some knowledge about it.
Tutorials are extremely detailed and contain lots of screenshots and pictures.
Goals & Scope of the course:
▫️ Learn ARM-Cortex M3 & M4 Architecture
▫️ Understand The Internals OF STM32 Microcontroller Hardware
▫️ Interface Various Peripherals Inside OF STM32 Microcontrollers
▫️ Develop Firmware In C-Programming Language Based On ST HAL & LL Driver Layers
▫️ Get Familiar With Interrupts / NVIC / EXTI & Inter-Module Signaling / DMA
▫️ Interface Various Display Units
▫️ Interface Various Input Units
▫️ Control Several Types OF Actuators
▫️ Do Some Serial Communication Via USART / SPI / I2C & Develop USB
▫️ Interface Various Sensors & Modules
▫️ ...and even more!
More information...
#articles #courses #stm32
Completely free course for those who want to start learning how to work with the most popular development board, improve their programming skills or refresh some knowledge about it.
Tutorials are extremely detailed and contain lots of screenshots and pictures.
Goals & Scope of the course:
▫️ Learn ARM-Cortex M3 & M4 Architecture
▫️ Understand The Internals OF STM32 Microcontroller Hardware
▫️ Interface Various Peripherals Inside OF STM32 Microcontrollers
▫️ Develop Firmware In C-Programming Language Based On ST HAL & LL Driver Layers
▫️ Get Familiar With Interrupts / NVIC / EXTI & Inter-Module Signaling / DMA
▫️ Interface Various Display Units
▫️ Interface Various Input Units
▫️ Control Several Types OF Actuators
▫️ Do Some Serial Communication Via USART / SPI / I2C & Develop USB
▫️ Interface Various Sensors & Modules
▫️ ...and even more!
More information...
#articles #courses #stm32
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🧠 Free Machine Learning course by Andrew Ng
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:
▫️ Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
▫️ Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
▫️ Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
More information:
Youtube playlist with lectures
Course on Coursera
Matlab implementation
Python implementation:
Part 1
Part 2
Part 3
Part 4
#courses #machine_learning #python #artificial_intelligence #programming
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:
▫️ Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
▫️ Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
▫️ Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
More information:
Youtube playlist with lectures
Course on Coursera
Matlab implementation
Python implementation:
Part 1
Part 2
Part 3
Part 4
#courses #machine_learning #python #artificial_intelligence #programming
This tutorial series is a practical guide to writing Linux kernel modules and drivers, with a focus on hardware access. It starts with an introduction to the Linux kernel, explaining its role as a hardware abstraction layer and its monolithic nature.
The series emphasizes Loadable Kernel Modules (LKMs), which allow dynamic driver loading to keep the kernel lightweight and flexible.
Key topics covered include:
The tutorials use
#courses #programming #linux #raspberry
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