by Jon Stace In introduction to Machine Learning with Tensor Flow Presented at the 2018 TechExeter Conference https://techexeter.uk

1. AN INTRODUCTION TO MACHINE LEARNING USING TENSORFLOW
JON STACE
@JONSTACE

2. What is Machine Learning
A technology that gives computers the ability to
learn using large datasets instead of hard coded
rules

3. What is it not?
Skynet
https://www.flickr.com/photos/31029865@N06/14997552775

4. What is it not?
Magic
https://en.m.wikipedia.org/wiki/File:Magic_wand.svg

5. What is it not?
Big Data
https://commons.wikimedia.org/wiki/File:BigData_2267x1146_white.png

6. Artificial Intelligence
Intelligence demonstrated by machines
Cognitive Functions
Machine Learning
Deep Learning

7. Why Bother?
Availability of tools
Availability of data
Don’t need to be a ML researcher/expert

8. Why not?
You don’t always need it
When you’ve already worked out the classification
You need to understand the options for algorithms

9. Examples of the use of ML
Titanic passenger list
Netflix
Retail
Importing Data
Insurance Risk Engineering

10. Basic Approach to Machine Learning

11. ML Categories
Supervised learning
Unsupervised learning
Reinforcement learning

12. Algorithms
Regression
Classification
Multi-class and two-class
Clustering
Anomaly Detection

14. Questions?

15. Machine Learning Technologies
TensorFlow
Azure Machine Learning
IBM Watson
R
mlpack
Microsoft Cognitive Toolkit

16. Why TensorFlow?
Flexible, comprehensive
Free!
High-level and low-level APIs
Works offline
GPU and TPU acceleration
Popular

17. Installation
Don’t have to stay on Python 2
Python 3.6, not 3.7
pip install tensorflow
Or
pip install tensorflow-gpu

18. Code Demo

19. Questions?

20. TensorFlow in other languages
C++ core library (C API)
Just the low-level API
Google maintain Python, Java, C and Go support
TensorFlow.js
C#
https://github.com/migueldeicaza/TensorFlowSharp

21. Code Demo

22. Summary
Machine Learning – what it is and isn’t
Example usages
Types of ML and the various algorithms
ML Technologies
TensorFlow in Python
TensorFlow in C#

23. Further Resources
Free Statistics books (https://openstax.org/details/books/introductory-statistics)
edX course on Machine Learning (https://courses.edx.org/courses/course-
v1:Microsoft+DAT263x+1T2018a/course/)
Microsoft Azure Essentials: Azure Machine Learning
(https://mva.microsoft.com/ebooks#9780735698178)
TensorFlow web site (https://www.tensorflow.org)
Google's machine learning crash course (https://developers.google.com/machine-
learning/crash-course/)
Quick guide to deep learning (https://medium.freecodecamp.org/want-to-know-how-deep-
learning-works-heres-a-quick-guide-for-everyone-1aedeca88076)
Microsoft page on algorithm choice (https://docs.microsoft.com/en-us/azure/machine-
learning/studio/algorithm-choice) includes link to PDF cheat sheet!

24. Questions?
TODO include slideshare link for this file