830 skills found · Page 21 of 28
L-A-Sandhu / TimeMeshA Python library for time series data preprocessing featuring advanced windowing strategies, normalization, and dataset splitting. Perfect for preparing time-dependent data for LSTM, Transformer, and other sequence models.
RKNITH / Volume Controller Hand Gesture PythonHere's a complete Python project for a Hand Gesture Volume Controller using webcam. This project uses OpenCV, MediaPipe, and pycaw (for controlling system volume on Windows). It adjusts the system volume based on the distance between the thumb and index finger.
KernFerm / Nvidia Installation GuideThis guide walks you through installing NVIDIA CUDA Toolkit 11.8, cuDNN, and TensorRT on Windows, including setting up Python packages like Cupy and TensorRT. It ensures proper system configuration for CUDA development, with steps for setting environment variables and verifying installation via cmd.exe
kyrre / Deathcon 2025 Memory AnalysisDEATHCon 2025 workshop on building custom memory analysis tools using the modern Python data ecosystem. This workshop teaches participants how to analyze Windows memory dumps using Volatility 3, marimo notebooks, DuckDB, and Ibis to create interactive triage dashboards for incident response.
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
lolin32 / ESP32Arduino core for ESP32 WiFi chip Build Status Need help or have a question? Join the chat at https://gitter.im/espressif/arduino-esp32 Development Status Installation Instructions: Using Arduino IDE Windows Mac OS Debian/Ubuntu Decoding Exceptions Using PlatformIO Using as ESP-IDF component ESP32Dev Board PINMAP Development Status Most of the framework is implemented. Most noticable is the missing analogWrite. While analogWrite is on it's way, there are a few other options that you can use: 16 channels LEDC which is PWM 8 channels SigmaDelta which uses SigmaDelta modulation 2 channels DAC which gives real analog output Installation Instructions Using through Arduino IDE ###Instructions for Windows Instructions for Mac Install latest Arduino IDE from arduino.cc Open Terminal and execute the following command (copy->paste and hit enter): mkdir -p ~/Documents/Arduino/hardware/espressif && \ cd ~/Documents/Arduino/hardware/espressif && \ git clone https://github.com/espressif/arduino-esp32.git esp32 && \ cd esp32/tools/ && \ python get.py Restart Arduino IDE Instructions for Debian/Ubuntu Linux Install latest Arduino IDE from arduino.cc Open Terminal and execute the following command (copy->paste and hit enter): sudo usermod -a -G dialout $USER && \ sudo apt-get install git && \ wget https://bootstrap.pypa.io/get-pip.py && \ sudo python get-pip.py && \ sudo pip install pyserial && \ mkdir -p ~/Arduino/hardware/espressif && \ cd ~/Arduino/hardware/espressif && \ git clone https://github.com/espressif/arduino-esp32.git esp32 && \ cd esp32/tools/ && \ python get.py Restart Arduino IDE Decoding exceptions You can use EspExceptionDecoder to get meaningful call trace. Using PlatformIO PlatformIO is an open source ecosystem for IoT development with cross platform build system, library manager and full support for Espressif ESP32 development. It works on the popular host OS: Mac OS X, Windows, Linux 32/64, Linux ARM (like Raspberry Pi, BeagleBone, CubieBoard). What is PlatformIO? PlatformIO IDE Quick Start with PlatformIO IDE or PlatformIO Core Integration with Cloud and Standalone IDEs - Cloud9, Codeanywehre, Eclipse Che (Codenvy), Atom, CLion, Eclipse, Emacs, NetBeans, Qt Creator, Sublime Text, VIM and Visual Studio Project Examples Using "Stage" (Git) version of Arduino Core Building with make makeEspArduino is a generic makefile for any ESP8266/ESP32 Arduino project. Using make instead of the Arduino IDE makes it easier to do automated and production builds. Using as ESP-IDF component Download and install esp-idf Create blank idf project (from one of the examples) in the project folder, create a folder called components and clone this repository inside mkdir -p components && \ cd components && \ git clone https://github.com/espressif/arduino-esp32.git arduino && \ cd .. && \ make menuconfig make menuconfig has some Arduino options "Autostart Arduino setup and loop on boot" If you enable this options, your main.cpp should be formated like any other sketch //file: main.cpp #include "Arduino.h" void setup(){ Serial.begin(115200); } void loop(){ Serial.println("loop"); delay(1000); } Else you need to implement app_main() and call initArduino(); in it. Keep in mind that setup() and loop() will not be called in this case. If you plan to base your code on examples provided in esp-idf, please make sure move the app_main() function in main.cpp from the files in the example. //file: main.cpp #include "Arduino.h" extern "C" void app_main() { initArduino(); pinMode(4, OUTPUT); digitalWrite(4, HIGH); //do your own thing } "Disable mutex locks for HAL" If enabled, there will be no protection on the drivers from concurently accessing them from another thread/interrupt/core "Autoconnect WiFi on boot" If enabled, WiFi will start with the last known configuration Else it will wait for WiFi.begin make flash monitor will build, upload and open serial monitor to your board ESP32Dev Board PINMAP Pin Functions Hint Sometimes to program ESP32 via serial you must keep GPIO0 LOW during the programming process
lukaskrasa / FBX SDK 2020.0.1 For Python 3.7.4 Windows Precompiled FBXSDK 2020.0.1 for Python 3.7.4
cgohlke / Python Curses BuildBuild python-curses wheels for Windows.
mahdi-reverted / ZKTeco Realtime With KivyZKTeco Attendance Realtime Monitoring developed with python-kivy for windows
Hommoner / EVEAIA Deep Learning Library based on python Keras and Tensorflow. Support for x86/x64 windows application.
microsoft / Python EtwtracePython extension for generating profiling data and stack samples for Windows Performance Analyzer
joaoajmatos / VINCA Python backdoor for spying on Windows machines
aryklein / DualBootMousePython script for Bluetooth pairing on dual boot systems (Linux and Windows 10)
tranquilit / TISbackupbackup server side executed python scripts for managing linux and windows system and application data backups, developed by adminsys for adminsys
MarkPengJZ / AutoClicker With ImageDetectionAn Auto Clicker with Image Detection for Windows coded with Python.
skippzz / Insightface 0.7.3 Cp312 Cp312 Win Amd64Prebuilt Windows wheel for insightface==0.7.3 (Python 3.12, cp312, amd64) — built for ComfyUI and Triton compatibility.
ecbftw / GrokevtGrokEVT is a collection of scripts built for reading Windows® NT/2K/XP/2K3 event log files. GrokEVT is released under the GNU GPL, and is implemented in Python.
artsalliancemedia / System MonitorLightweight Unix & WIndows system monitoring solution in python, designed to HTTP POST back to a centralised endpoint periodically. Useful for gather performance metrics from multiple machines.
abhirajD / PyKinectWrapper to expose Kinect for Windows v2 API in Python
scorpion004 / Luna Voice AssistantThis is a voice assistant program for windows made of python language. No need of IDE and command to run this program.
