Corso di formazione TensorFlow for Image Recognition

Codice del corso

tfir

Durata

28 ore (generalmente 4 giorni pause incluse)

Requisiti

  • Python

Overview

Questo corso esplora, con esempi specifici, l'applicazione del flusso tensoriale ai fini del riconoscimento delle immagini

Pubblico

Questo corso è destinato agli ingegneri che desiderano utilizzare TensorFlow ai fini del riconoscimento delle immagini

Dopo aver completato questo corso, i delegati saranno in grado di:

  • comprendere la struttura e i meccanismi di implementazione di TensorFlow
  • svolgere attività e configurazione di installazione / ambiente di produzione / architettura
  • valutare la qualità del codice, eseguire il debug, il monitoraggio
  • implementare la produzione avanzata come modelli di formazione, creazione di grafici e registrazione

Machine Translated

Struttura del corso

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.

Recensioni

★★★★★
★★★★★

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