Machine Learning & AI - A Gentle Introduction
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This document provides a gentle introduction to machine learning and AI. It discusses how machine learning works by training algorithms on large datasets to recognize patterns without being explicitly programmed. Examples are given of how machine learning is used in applications like image recognition, speech recognition, and machine translation. The recent rise of machine learning is driven by the abundance of big data from many online sources and the availability of affordable computing power like GPUs. The future of machine learning is unpredictable but areas like quantum computing may further advance the field.
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Machine Learning & AI - A Gentle Introduction
- 1. Machine Learning & AI A gentle introduction Frank Salliau KTech Meetup Kortrijk– Dec 13 2017
- 5. (Almost) reality in 2017
- 10. Reality in 2017
- 11. More examples…
- 15. “Alexa, check my calendar” “Alexa, what is machine learning? “Alexa, play Spotify
- 17. What is it all about?
- 20. What is Machine Learning? “The science of getting computers to act without being explicitly programmed” - Andrew Ng (Stanford/DeepLearning AI)
- 21. Challenge Recognize dogs in images
- 22. What a human sees DOG NOT A DOG
- 23. What a computer sees ??? ???
- 24. What do we need? TRAINING DATA
- 25. Labeled training set (dog/not dog) > 1000 images Untrained Neural Network Training phase DOG (1) NOT DOG (0)
- 26. Unlabeled image Trained Neural Network Prediction phase 87% DOG 13% NOT DOG
- 27. Why is this booming now?
- 29. What are the drivers?
- 30. (BIG) DATA
- 31. Byte Byte : one grain of rice Credits: David Wellman, http://www.slideshare.net/dwellman/what-is-big-data-24401517
- 32. Kilobyte Byte : one grain of rice Kilobyte : cup of rice
- 33. Megabyte Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice
- 34. Gigabyte Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice Gigabyte : 3 Semi trucks
- 35. Terabyte Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice Gigabyte : 3 Semi trucks Terabyte : 2 Container Ships
- 36. Petabyte Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice Gigabyte : 3 Semi trucks Terabyte : 2 Container Ships Petabyte : Blankets Manhattan
- 37. One ByteExabyte Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice Gigabyte : 3 Semi trucks Terabyte : 2 Container Ships Petabyte : Blankets Manhattan Exabyte : Blankets west coast states
- 38. Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice Gigabyte : 3 Semi trucks Terabyte : 2 Container Ships Petabyte : Blankets Manhattan Exabyte : Blankets west coast states Zettabyte : Fills the Pacific Ocean Zettabyte
- 39. Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice Gigabyte : 3 Semi trucks Terabyte : 2 Container Ships Petabyte : Blankets Manhattan Exabyte : Blankets west coast states Zettabyte : Fills the Pacific Ocean Yottabyte : A EARTH SIZE RICE BALL! Yottabyte
- 40. Byte : one grain of rice Kilobyte : cup of rice Megabyte : 8 bags of rice Gigabyte : 3 Semi trucks Terabyte : 2 Container Ships Petabyte : Blankets Manhattan Exabyte : Blankets west coast states Zettabyte : Fills the Pacific Ocean Yottabyte : A EARTH SIZE RICE BALL! Hobbyist Desktop Internet Big Data The Future
- 41. 1 Yottabyte 1 Xenottabyte 1 Shilentnobyte 1 Domegemegrottebyte 1 Icosebyte 1 Monoicosebyte
- 42. Where does all this data come from?
- 44. The Power of the Crowd
- 47. Affordable and scalable computing power
- 48. Brainz
- 49. Finding your way in the machine learning maze
- 52. Tries to find patterns in unlabeled data “Clustering”
- 55. Fits a model to previously labeled data (e.g “dog” or “not dog”)
- 56. Regression
- 57. Classification
- 58. Deep Learning
- 59. Form of supervised learning using neural networks with many layers… hence “deep”
- 62. Google Translate
- 63. Google Translate
- 64. Google Deep Dream (sometimes nightmare)
- 73. Good course on RL: Udemy - “Lazy Programmer”
- 77. Getting started
- 78. My personal journey started on… Andrew Ng • Machine Learning • Deep Learning • Many others…
- 79. More interesting stuff (courses/books) on…
- 84. D3.js - Open Source
- 85. Resource-intensive computing • Machine Learning algorithms can be computationally intensive • Repeated multiplication of large matrices • Especially true for neural networks • Runs for hours on laptop using CPU
- 86. Rise of the GPU
- 88. Machine Learning / Data Driven Projects
- 90. Quality of data: Garbage In Garbage Out
- 93. Iterative & explorative process
- 94. Caveats
- 95. ”All models are wrong, but some are useful” George E.P. Box
- 99. “Software engineers will be obsolete by 2060” (we’ll see)
- 100. Do you trust the black box?
- 102. Interesting read…
- 103. The Future Is unpredictable, but…
- 106. Microsoft releases Q# Quantum Dev Kit https://blogs.microsoft.com/ai/2017/12/11/future-quantum-microsoft-releases-free-preview- quantum-development-kit/
- 110. Frank Salliau Independent Data Scientist & Machine Learning Expert http://www.mentat-it.be https://be.linkedin.com/in/franksalliau frank.salliau@mentat-it.be
Notes de l'éditeur
- So, why is all this booming now.
- How to find your way in the ML jungle