Machine Learning with TensorFlow
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Machine Learning with TensorFlow

Tackle common machine learning problems with Google’s TensorFlow library and build deployable solutions
2.2 (11 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
217 students enrolled
Created by Packt Publishing
Last updated 2/2017
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Current price: $10 Original price: $125 Discount: 92% off
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  • 1.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
  • This video is a clear, step-by-step demonstration, enabling you to understand the new heights in machine learning and will empower you to implement it in your environment.

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.

About The Author

Shams Ul Azeem is an undergraduate of NUST Islamabad, Pakistan, in Electrical Engineering. He has a great interest in the field of computer science and has started his journey from Android Development.Now he’s pursuing his career in Machine Learning, particularly in Deep Learning, by doing medical-related freelance projects with different companies.

He was also a member of RISE lab, NUST, and has a publication in the IEEE International Conference, ROBIO as a co-author on Designing of motions for humanoid goal keeper robots.

Who is the target audience?
  • This video 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. Also aimed at software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow’s unique features.
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Curriculum For This Course
19 Lectures
Getting Started with Deep Learning
5 Lectures 18:26

This video will provide an overview of the entire course.

Preview 03:48

Get the user excited about ML; a quick view of various platforms and key products using the ML tools (photo search, Siri, and so on). 

Introducing Deep Learning

We will install the TensorFlow platform and associated prerequisites on the Mac OS X operating system.

Installing TensorFlow on Mac OSX

Since TensorFlow does not function natively on Windows, we will cover Virtual Machine setup so Windows users can run Linux on a VM.

Installation on Windows – Pre-Reqeusite Virtual Machine Setup

We will cover TensorFlow installation on Linux. This also covers installation on Windows via a Virtual Machine running Linux. 

Installation on Windows/Linux
Your First Classifier
5 Lectures 16:37

Which dataset do we use for our first classifier, and what attributes do we consider? Let's use a dataset of letters in numerous typefaces.

Preview 03:01

We just spent a good amount of time obtaining and preparing data, are we expected to do this for each run? No, you'll now learn to automate the initial steps used to feed the machine learning process.

Automating Data Preparation

How do we represent images internally within the machine learning environment? We'll hold image data in a stack of matrices. 

Understanding Matrix Conversions

So now that we have looked upon the major concepts, the question remains how do we actually train the classifier in code? We are now going to code and train a classifier using TensorFlow. 

The Machine Learning Life Cycle

Once training is complete, how are the results interpreted and measured? We'll use the training set, with known labels to measure how well our trained system performs. 

Reviewing Outputs and Results
The TensorFlow Toolbox
4 Lectures 20:39

How do we monitor the internals of our training setup and execution? We'll use TensorBoard to view our network architecture and probe values through the training. 

Preview 05:08

How do we monitor the scalars and tensors on TensorBoard? We'll set up code for log specific variables and TensorBoard reads these structured logs. 

TensorBoard Events and Histograms

How do we view the network architecture we've built for our training? We'll push the graph to a TensorBoard readable log and view it on Graph Explorer. 

The Graph Explorer

How do we actually apply what you've just learned to our own project? You'll learn to go through individual changes to our previous project.

Our Previous Project on TensorBoard
Cats and Dogs – Convolutional Neural Networks
5 Lectures 18:49

How do we process images with more complex and better suited neural networks? For this purpose, we will use the well-known convolutional neural networks also known as CNNs. 

Preview 04:44

How do CNNs work and what does their model look like? We are going to talk about the basic model architecture of CNN. 

Convolutional Neural Networks

How to implement a basic CNN? We will look into the coding of a CNN. 

Programming a CNN

How does it look after coding? We will visualize the implemented CNN in TensorFlow. 

Using TensorBoard on Our CNN

How much better CNNs are as compared to normal fully connected networks? We will compare the performances of the two models on CIFAR-10 dataset. 

CNN Versus Fully Connected Network Performance
About the Instructor
Packt Publishing
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