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Tackle common machine learning problems with Google’s TensorFlow library and build deployable solutions.


Course Description

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 course 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.

Learning Outcomes

  • 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

Pre-requisite

You should be familiar with Python and matrix math.


Who is this course intended for?

This course is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. This course is also for software developers who wish to learn deep learning by example. With a particular focus on solving deep learning problems from several real-world sources (notMNIST, CIFAR10) using TensorFlow’s unique features, no commercial domain knowledge is required to take this course. Familiarity with Python and matrix math is expected though.

Your Instructor

Packt Publishing

Packt has been committed to developer learning since 2004. A lot has changed in software since then – but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content – more than 4000 books and video courses -Packt’s mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages to cutting-edge data analytics, and DevOps, Packt takes software professionals in every field to what’s important to them now.

From skills that will help you to develop and future-proof your career to immediate solutions to everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Course Curriculum

Getting Started with Deep Learning
The Course Overview 00:00:00
Introducing Deep Learning 00:00:00
Installing TensorFlow on Mac OSX 00:00:00
Installation on Windows – Pre-Reqeusite Virtual Machine Setup 00:00:00
Installation on Windows/Linux 00:00:00
Your First Classifier
The Hand-Written Letters Dataset 00:00:00
Automating Data Preparation 00:00:00
Understanding Matrix Conversions 00:00:00
The Machine Learning Life Cycle 00:00:00
Reviewing Outputs and Results 00:00:00
The TensorFlow Toolbox
Getting Started with TensorBoard 00:00:00
TensorBoard Events and Histograms 00:00:00
The Graph Explorer 00:00:00
Our Previous Project on TensorBoard 00:00:00
Cats and Dogs – Convolutional Neural Networks
Fully Connected Neural Networks 00:00:00
Convolutional Neural Networks 00:00:00
Programming a CNN 00:00:00
Using TensorBoard on Our CNN 00:00:00
CNN Versus Fully Connected Network Performance 00:00:00

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