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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.