TensorFlow for Image Recognition Treningskurs

Last updated

Kurskode

tfir

Varighet

28 timer (vanligvis 4 dag inkludert pauser)

Krav

  • Python

Oversikt

Dette kurset utforsker, med spesifikke eksempler, anvendelsen av Tensor Flow til formålet med gjenkjenning

Publikum

Dette kurset er beregnet på ingeniører som søker å bruke TensorFlow til bildegjenkjenning

Etter fullført kurs vil delegatene kunne:

  • forstå TensorFlow struktur og distribusjonsmekanismer
  • utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
  • vurdere kodekvalitet, utfør feilsøking, overvåking
  • implementere avansert produksjon som treningsmodeller, bygge grafer og logging

Machine Translated

Kursplan

Machine Learning and Recursive Neural Networks (RNN) basics

  • NN and RNN
  • Backpropagation
  • Long short-term memory (LSTM)

TensorFlow Basics

  • Creation, Initializing, Saving, and Restoring TensorFlow variables
  • Feeding, Reading and Preloading TensorFlow Data
  • How to use TensorFlow infrastructure to train models at scale
  • Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics 101

  • Tutorial Files
  • Prepare the Data
    • Download
    • Inputs and Placeholders
  • Build the Graph
    • Inference
    • Loss
    • Training
  • Train the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluate the Model
    • Build the Eval Graph
    • Eval Output

Advanced Usage

  • Threading and Queues
  • Distributed TensorFlow
  • Writing Documentation and Sharing your Model
  • Customizing Data Readers
  • Using GPUs¹
  • Manipulating TensorFlow Model Files

TensorFlow Serving

  • Introduction
  • Basic Serving Tutorial
  • Advanced Serving Tutorial
  • Serving Inception Model Tutorial

Convolutional Neural Networks

  • Overview
    • Goals
    • Highlights of the Tutorial
    • Model Architecture
  • Code Organization
  • CIFAR-10 Model
    • Model Inputs
    • Model Prediction
    • Model Training
  • Launching and Training the Model
  • Evaluating a Model
  • Training a Model Using Multiple GPU Cards¹
    • Placing Variables and Operations on Devices
    • Launching and Training the Model on Multiple GPU cards

Deep Learning for MNIST

  • Setup
  • Load MNIST Data
  • Start TensorFlow InteractiveSession
  • Build a Softmax Regression Model
  • Placeholders
  • Variables
  • Predicted Class and Cost Function
  • Train the Model
  • Evaluate the Model
  • Build a Multilayer Convolutional Network
  • Weight Initialization
  • Convolution and Pooling
  • First Convolutional Layer
  • Second Convolutional Layer
  • Densely Connected Layer
  • Readout Layer
  • Train and Evaluate the Model

Image Recognition

  • Inception-v3
    • C++
    • Java

¹ Topics related to the use of GPUs are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

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