TensorFlow for Image Recognition Training Course

Machine Learning and Recursive Neural Networks (RNN) basics

• NN and RNN
• Backprogation
• 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.