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TensorFlow Tutorial – Deep Learning

By Prosyscom
In March 10, 2018
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Deep Learning with TensorFlow:
TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
This video addresses common commercial machine learning problems using Google’s TensorFlow library. It will not only help you discover what TensorFlow is and how to use it, but will also show you the unbelievable things that can be done in machine learning with the help of examples/real-world use cases. We start off with the basic installation of Tensorflow, moving on to covering the unique features of the library such as Data Flow Graphs, training, and visualization of performance with TensorBoard—all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section.
You Will Learn Set up basic and advanced TensorFlow installations Deep dive into training, validating, and monitoring training performance Set up and run cross-sectional examples using images Create pipelines to deal with real-world input data Be empowered to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage

Table of contents

SECTION 1
Getting Started
The Course Overview
Introducing Deep Learning
Installation on Mac OSX
Installation on Windows – Pre-Requisite Virtual Machine Setup
Installation on Windows/Linux
SECTION 2
Your First Classifier
The Hand-Written Letters Dataset
Automating Data Preparation
Understanding Matrix Conversions
The Machine Learning Life Cycle
Reviewing Outputs and Results
The TensorFlow Toolbox

SECTION 3
Getting Started with TensorBoard
TensorBoard Events and Histograms
The Graph Explorer
Our Previous Project on TensorBoard
Cats and Dogs – Convolutional Neural Networks

SECTION 4
Fully Connected Neural Networks
Convolutional Neural Networks
Programming a CNN
Using TensorBoard on Our CNN
CNN Versus Fully Connected Network Performance

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