TensorFlow for Image Recognition Träningskurs

Last updated

Kurskod

tfir

Varaktighet

28 timmar (vanligtvis 4 dag inklusive pauser)

Krav

  • Python

Översikt

Denna kurs undersöker, med specifika exempel, tillämpningen av Tensor Flow för syftena med bildigenkänning

Publik

Kursen är avsedd för ingenjörer som vill använda TensorFlow för TensorFlow

Efter avslutad kurs kommer deltagarna att kunna:

  • förstå TensorFlow struktur och distributionsmekanismer
  • utföra installations- / produktionsmiljö / arkitekturuppgifter och konfiguration
  • utvärdera kodkvalitet, utföra felsökning, övervakning
  • implementera avancerad produktion som träningsmodeller, bygga grafer och logga

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.

Vittnesmål

★★★★★
★★★★★

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