Showing 2 open source projects for "physical memory mdd"

View related business solutions
  • Application Monitoring That Won't Slow Your App Down Icon
    Application Monitoring That Won't Slow Your App Down

    AppSignal's Rust-based agent is lightweight and stable. Already running in thousands of production apps.

    Full APM with errors, performance, logs, and uptime monitoring. 99.999% uptime SLA on the platform itself.
    Start Free
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do bestβ€”building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • 1
    Eva AI

    Eva AI

    Eva is an A.I. assistant that helps users multi-task.

    ...Tell Eva "Listen" or "Hey listen" followed by a command. For more instructions, check the instruction manual included in the application. [Update] * πŸ†• Removed paged memory cleanup * πŸ†• Re-added physical model switch-up * πŸ†• Added automatic microphone audio level maximisation * πŸ†• Re-calibrated the * 🐞 Re-added the wake word engine reset mechanism * 🐞 Fixed UI related issues regarding threading * 🐞 Fixed thread synchronisation bugs
    Downloads: 2 This Week
    Last Update:
    See Project
  • 2
    LSTMs for Human Activity Recognition

    LSTMs for Human Activity Recognition

    Human Activity Recognition example using TensorFlow on smartphone

    LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the use of sequential neural network architectures, the system learns patterns in motion data that correspond to activities such as walking, sitting, standing, or climbing stairs. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB