TINY MACHINE LEARNING COURSE

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TINY MACHINE LEARNING - LESSON 1

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. Introduction
  4. We prepare our AI Lab
  5. Installation and Configuration of the Arduino Nano 33 BLE Sense
  6. How to upload the sketch and how to run it on Nano BLE Sense
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TINY MACHINE LEARNING - LESSON 2

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. Let’s practice with our new Nano BLE Sense board
  4. APDS 9960 – Digital Proximity Sensor, RGB Light and Gestures
  5. Proximity detection
  6. Gesture detection
  7. Color detection
  8. PAI-001: Color detection
  9. Analysis of the sketch of the AI-001 Project – Color detection
  10. PAI-002: Gestures detection
  11. Analysis of the sketch of the AI-002 Project – Gesture detection
  12. PAI-003: Proximity detection
  13. Analysis of the sketch of the AI-003 Project – Proximity detection
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TINY MACHINE LEARNING - LESSON 3

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. The Humidity, Temperature and Pressure sensors of the Nano BLE Sense
  4. PRESSURE SENSOR LPS22HB
  5. HUMIDITY AND TEMPERATURE SENSOR HTS221
  6. PAI-004: Measure Humidity and Temperature with HTS221
  7. Analysis of the sketch of project AI-004 – Measure Humidity and Temperature with HTS221
  8. PAI-005: Measurement of Barometric Pressure and Temperature with LPS22HB
  9. Sketch Analysis of Project AI-005 – Pressure and Temperature with LPS22HB
  10. PAI-006: Simple Weather Station
  11. Analysis of the sketch of the PROJECT AI-006 – Simple Weather Station
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TINY MACHINE LEARNING - LESSON 4

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. Inertia and the IMU Sensor – LSM9DS1
  4. PAI-007: Measure acceleration along Cartesian axes with LSM9DS1
  5. Analysis of Project AI-007 – Measure x, y, and z acceleration with LSM9DS1
  6. PAI-008: Measure angular velocity with LSM9DS1
  7. Analysis of Project AI-008 – Measure Angular Velocity with LSM9DS1
  8. PAI-009: Magnetic Field Measurement on Cartesian Axes with LSM9DS1
  9. Analysis of the AI-009 Project – 3D Magnetic Field Measurement with LSM9DS1
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TINY MACHINE LEARNING - LESSON 5

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. MP34DT05 Omnidirectional Digital Microphone
  4. The Capacitor
  5. The RGB LED on the Arduino Nano 33 BLE Sense
  6. PAI-010: The RGB LED on board
  7. Sketch analysis of Project AI-010 – The RGB LED on board
  8. PAI-011: Sound Analyzer with Microphone MP34DT05
  9. PAI-012: RGB LED driven by sound analysis
  10. Sketch analysis of Project AI-012 – RGB LED driven by sound analysis
  11. The BLE module in Arduino Nano 33 BLE Sense
  12. PAI-013: Nano RGB LED switching on via Iphone
  13. Sketch analysis of Project AI-013 – Nano RGB LED switching on via Iphone
GO TO THE LESSON

TINY MACHINE LEARNING - LESSON 6

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. Artificial Intelligence and Machine Learning
  4. Creating an Artificial Intelligence with Deep Learning
  5. Installation of the TensorFlow Lite Library
  6. PAI-014: Hello World – TinyML
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TINY MACHINE LEARNING - LESSON 7

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. First Step for the creation of the Trained Model “Hello World”
  4. The Training Dataset
  5. PAI-015: The first steps in Google Colab
  6. PAI-016: Realization of the “Hello World” Dataset on Colab
  7. PAI-017: Data for Training, Data for Validation and Data for Tests
  8. PAI-018: Completing Datasets for Model Training
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TINY MACHINE LEARNING - LESSON 8

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. Deep Learning and the Neural Network
  4. Activation functions of neurons
  5. Sigmoid function
  6. Smoothstep function
  7. Hyperbolic Tangent Function – Tanh
  8. ReLU function
  9. Definition of the Model to train for Hello World with Keras
  10. PAI-019: Hello World Model (Base)
  11. Training the Hello World Model (Basic)
  12. Graphical representation of training data
  13. PAI-020: Hello World Model (Improved)
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TINY MACHINE LEARNING - LESSON 9

TOPICS INDEX

  1. Warnings
  2. Copyright Notice
  3. Packing the Hello World Model to make a Sketch
  4. Resizing or Quantizing the Model
  5. Realization of the Application (Sketch) that uses the TinyML Model
  6. Conclusions
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