TensorFlow Lite for Embedded Linux Treningskurs

Last updated

Kurskode

tensorflowlite

Varighet

21 timer (vanligvis 3 dag inkludert pauser)

Krav

  • An understanding of deep learning concepts
  • Python programming experience
  • A device running embedded Linux (Raspberry Pi, Coral device, etc.)

Audience

  • Developers
  • Data scientists with an interest in embedded systems

Oversikt

TensorFlow Lite er en åpen kildekode dyp læring rammeverk for å kjøre modeller på mobile og innebygde enheter med begrensede datamaskin- og hukommelsesressurser.

Denne instruktørledede, live-trening (online eller online) er rettet mot utviklere som ønsker å bruke TensorFlow Lite til å implementere dyp læringsmodeller på innebygde enheter.

Ved slutten av denne treningen vil deltakerne være i stand til å:

    Installere og konfigurere Tensorflow Lite på en innebygd enhet. Forstå begreper og komponenter underliggende TensorFlow Lite. Konverter eksisterende maskinlæringsmodeller til TensorFlow Lite format for utførelse på innebygde enheter. Arbeide innenfor begrensningene til små enheter og TensorFlow Lite, mens du lærer hvordan du utvider standardkapasitetene dine. Utvikle dyp læringsmodeller på innebygde enheter som kjører Linux for å løse fysiske verdensproblemer som å gjenkjenne bilder og stemme, forutse mønstre, og initiere bevegelser og svar fra roboter og andre innebygde systemer i feltet.

Format av kurset

    Interaktiv forelesning og diskusjon. Mange øvelser og praksis. Hands-on implementering i et live-lab miljø.

Kurs tilpassingsalternativer

    For å be om en tilpasset opplæring for dette kurset, vennligst kontakt oss for å arrangere.

Machine Translated

Kursplan

Introduction

  • TensforFlow Lite's game changing role in embedded systems and IoT

Overview of TensorFlow Lite Features and Operations

  • Addressing limited device resources
  • Default and expanded operations

Setting up TensorFlow Lite

  • Installing the TensorFlow Lite interpreter
  • Installing other TensorFlow packages
  • Working from the command line vs Python API

Choosing a Model to Run on a Device

  • Overview of pre-trained models: image classification, object detection, smart reply, pose estimation, segmentation
  • Choosing a model from TensorFlow Hub or other source

Customizing a Pre-trained Model

  • How transfer learning works
  • Retraining an image classification model

Converting a Model

  • Understanding the TensorFlow Lite format (size, speed, optimizations, etc.)
  • Converting a model to the TensorFlow Lite format

Running a Prediction Model

  • Understanding how the model, interpreter, input data work together
  • Calling the interpreter from a device
  • Running data through the model to obtain predictions

Accelerating Model Operations

  • Understanding on-board acceleration, GPUs, etc.
  • Configuring Delegates to accelerate operations

Adding Model Operations

  • Using TensorFlow Select to add operations to a model.
  • Building a custom version of the interpreter
  • Using Custom operators to write or port new operations

Optimizing the Model

  • Understanding the balance of performance, model size, and accuracy
  • Using the Model Optimization Toolkit to optimize the size and performance of a model
  • Post-training quantization

Troubleshooting

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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