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AIoT Home Trainer - EDU Market
AIoT Home Trainer

AIoT Home Trainer

Features

  • AI and IoT convergence training equipment using 2D model of living room of home
  • Main module supporting AI acceleration calculation, multimedia and various IoT sensors are integrated into the base board
  • The main module is selectable between a 128-core GPU supercomputer for edge devices or a Cortex-A72 quadcore processor with tensor processor unit
  • 4 inch TFT LCD with 800×480 resolution and 8M pixel high resolution camera
  • Provides Gigabit Ethernet, dual band Wi-Fi(2.4GHz, 5GHz) and Bluetooth 4.2 or 5.0
  • Digital microphones and speakers support cloud-based speech recognition and audio playback
  • 4 dedicated expansion interfaces support various IoT sensor modules
  • Positioning sensors and actuators by creating 2D models of living rooms in real homes to increase immersion
  • Soda OS, the exclusive AIoT operating system, and Pop library
  • Interpreter-based C/C++ development environments optimized for programming beginners, including Python 3
  • A dedicated web browser-based learning environment for training Python 3 and C/C++ simultaneously on PCs and tablets
  • mDNS/DNS-SD based distributed name resolution, network service publishing and discovery support
  • Open Integrated development environment based on Visual Studio Code for professional application development
  • Educational contents for IoT sensor control, multimedia and AI
  • Provides 8 types of IoT sensor modules connected to a dedicated expansion interface
  • Contains a supercomputer up to 21TOPS supporting all AI frameworks.
Categories: , , ,

Description

Introduction:

Configuration and Practice Environment of AIoT Home
Python and Linux 101
IoT Application Technology
Sensor Control:
File and DB-Based Data Persistence
Audio Recording and Playback
Google Text-to-Speech Converter
Google Assistant and User Device Actions
Camera and Sensor Applications.

AI Technology:

Numpy for Fast Multidimensional Matrix Operations
Pandas for Time Series and Tabular Data Analysis
Matplotlib for Data Visualization
Supervised and Unsupervised Learning
Theory & Practice for Pop.AI-based Linear and Logistic Regression Algorithm
Theory & Practice for Pop.AI-based Perceptron
Theory & Practice for Pop.AI-based ANN, DNN, and CNN
Theory & Practice for Pop.AI & OpenAI DQN-based Reinforcement Learning
Understanding Tensorflow

 

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