TensorFlow for Image Recognition Training Course

This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Course Outline

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 DataDownloadInputs and Placeholders
• Build the GraphInferenceLossTraining
• Train the ModelThe GraphThe SessionTrain Loop
• Evaluate the ModelBuild the Eval GraphEval 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

• OverviewGoalsHighlights of the TutorialModel Architecture
• Code Organization
• CIFAR-10 ModelModel InputsModel PredictionModel Training
• Launching and Training the Model
• Evaluating a Model
• Training a Model Using Multiple GPU Cards1Placing Variables and Operations on DevicesLaunching 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-v3C++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.