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            課程目錄：TensorFlow for Image Recognition培訓
          
        
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    課程大綱：
        
    
    
        
      
               TensorFlow for Image Recognition培訓
        
      
        
      
         
        
      
         
        
      
         
        
      
        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.