6587 Open source projects that are alternatives of or similar to Nanovna

STM32F103 USB CDC CMSIS

Customizable Bootloader for STM32 microcontrollers. This example demonstrates how to perform in-application-programming of a firmware located on an external SD card with FAT32 file system.

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Mongoose OS - an IoT Firmware Development Framework. Supported microcontrollers: ESP32, ESP8266, CC3220, CC3200, STM32F4, STM32L4, STM32F7. Amazon AWS IoT, Microsoft Azure, Google IoT Core integrated. Code in C or JavaScript.

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This is the fast avr programmer for AVR MCUs based on cheap stm32f103c8t6 board with usb-to-serial support.

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A communicative piece of wearable electronics.

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UART bootloader for STM32 microcontroller.

Flipper Zero firmware source code

JTAG probe firmware for STM32F1

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stm32f072 dev board with arduino nano alike pinout

LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.

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Jupyter notebooks in Russian. Introduction to Python, basic algorithms and data structures

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The 3rd edition of course.fast.ai

Training and experimentation code used for "Stacked Hourglass Networks for Human Pose Estimation"

code for deep learning courses

The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.

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Unofficial iRobot Roomba and Braava (i7/i7+, 980, 960, 900, e5, 690, 675, m6, etc) node.js library (SDK) to control your robot

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

Person Re-ranking (CVPR 2017)

Working with and analyzing Wikipedia Data

全栈数据工程师养成攻略

Abuse of Google Colab for cracking hashes. 🐧

Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star.

Creating fun photomosaics, GIFs, and murals from your family pictures using ML & similarity search

Official SRFlow training code: Super-Resolution using Normalizing Flow in PyTorch

Ways of doing Data Science Engineering and Machine Learning in R and Python

Silero Models: pre-trained STT models and benchmarks made embarrassingly simple

A Jupyter notebook viewer for macOS

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

TernausNetV2: Fully Convolutional Network for Instance Segmentation

Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

A tutorial on the most important features and idioms of Scala that you need to use Spark's Scala APIs.

Different Types of Stock Analysis in Python, R, Matlab, Excel, Power BI

本仓库将PyTorch官方书籍《Deep learning with PyTorch》（基本摘录版）翻译成中文版并给出可运行的相关代码。

A multi-task learning example for the paper https://arxiv.org/abs/1705.07115

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