Course Code

tf101

Duration

21 hours (usually 3 days including breaks)

Requirements

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

Overview

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

Course Outline

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.