Deep Learning with TensorFlow

• Statistics
• Python
• (optional) A laptop with NVIDIA GPU that supports CUDA 8.0 and cuDNN 5.1, with 64-bit Linux installed

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging

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

• 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

¹ The Advanced Usage topic, “Using GPUs”, is not available as a part of a remote course. This module 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.