Intro to Machine Learning by Google Product Manager
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Ground breaking technologies like neural-net algorithms along with the ability to run much more powerful computation started a new era in Machine Learning, ML. We're now able to use Machine Learning for products in ways we could only dream about and companies from all around the world are starting to seize the opportunity.
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Intro to Machine Learning by Google Product Manager
- 1. Intro to Machine Learning by Google Product Manager www.productschool.com
- 2. FREE INVITE Join 20,000+ Product Managers on productschool.com/slack-community
- 5. Data Analytics for Managers 2-month part-time Courses
- 7. Include @productschool and #prodmgmt at the end of your tweet Tweet to get a free ticket for our next Event!
- 9. Machine Learning for PMs By Ronnie Falcon, PM at Google
- 10. We’ll answer: 1. What role does ML have in industry? 2. What are the basics every PM needs to know about ML? 3. How can PMs contribute to ML-driven Products?
- 11. 1. What role does ML have in industry?
- 12. Welcome to the ML era!
- 13. - E-commerce - Healthcare - Financial trading - Data security - Personal security - Marketing personalization - Fraud detection - Recommendation systems - Natural language processing - Image recognition - Automotives - Spam detection - Augmented reality - Virtual reality - Language translations - Gaming - Chatbots - Loan approvals
- 17. Diagnosis in medical imaging
- 20. AlphaGo beats Lee Sedol in Go
- 21. Why now?
- 22. 1. More computing power Moore’s Law: number of transistor on a square inch circuit doubles every 2 years
- 23. 2. More data
- 25. 4. Growth in funding
- 26. 2. What are the basics every PM needs to know about ML?
- 28. AI definition: The broader concept of machines being able to carry out tasks in a way that we would consider “smart” Bernard Marr
- 29. A current application of AI, based around the idea that we should be able to give machines access to data and let them learn for themselves ML definition: Bernard Marr
- 30. Task Data Tech Evaluation ML Model The core of a machine learning system; creates predictions or decisions based on data
- 31. Task: Distinguish between a hipster and an old man.
- 33. = Old man White hair Glasses Walking stick Data
- 34. Tech
- 35. Evaluation
- 37. Task Data Tech Evaluation ML Model
- 39. Classification
- 42. ?= P(lives in bushwick) Regression
- 43. Day 1 Day 2 Day 3 Day 4? Sequences
- 44. ➔Examples ◆ Labels ◆ Features ➔Approach ◆ Supervised ◆ Semi supervised ◆ Unsupervised Data
- 45. Examples
- 47. Approach
- 48. Hipster Old man Supervised learning Hipster Hipster Hipster Old man Old man Old man
- 51. Clustering
- 54. ➔Linear ➔Non Linear ➔Deep Neural Networks ➔Reinforcement Tech
- 55. Embedding
- 59. Features Hidden Layers Output Layer Deep Neural Networks
- 60. Features Hidden Layers Output Layer W=1 Deep Neural Networks
- 61. Features Hidden Layers Output Layer W=0 W=0.5 Deep Neural Networks
- 62. Features Hidden Layers Output Layer Deep Neural Networks
- 63. Agent Environment Start to finish = 2 Color = 1 White = 0 Black = -3 Reinforcement Learning
- 64. Agent Environment Action Start to finish = 2 Color = 1 White = 0 Black = -3 Reinforcement Learning
- 65. Agent Environment Action Reward = 4 Start to finish = 2 Color = 1 White = 0 Black = -3 Reinforcement Learning
- 66. Agent Environment Action Start to finish = 2 Color = 1 White = 0 Black = -3 Reinforcement Learning
- 67. Agent Environment Action Reward = 7 Start to finish = 2 Color = 1 White = 0 Black = -3 Reinforcement Learning
- 69. Recall: out of all the hipsters in the world, how many can the algorithm successfully find?
- 70. Precision: given one example, what is the chance that the algorithm will classify it correctly as a hipster
- 71. Testing examples for evaluation
- 72. 1. Classification 2. Regression 3. Sequences 1. Examples ◆ Labels ◆ Features 2. Approach ◆ Supervised ◆ Semi-supervised ◆ Unsupervised 1. Linear 2. Non Linear 3. Neural 4. Reinforcement Task Data Tech 1. Recall 2. Precision Evaluation
- 73. How can PM's contribute to ML-driven Products?
- 74. 1. Know your ML (in high level)
- 75. 2. Help the team decide what to optimize for
- 76. Optimize for recall or precision? High recall = 95% of Francis’s photos will be suggested for an auto-tag High precision = 95% of the suggested photo for an auto-tag really have Francis
- 77. Sometimes ML isn’t the answer
- 78. 3. Think about ML early in the product design
- 79. Some things are only possible because of ML
- 80. 4. Collect the right data
- 81. Make sure your data isn’t biased
- 83. Let users give feedback (and log it!!)
- 84. What if you have no data?
- 85. Crowdsource!
- 87. 5. Think about the user experience
- 88. Think about how users will react to new features
- 89. Think about whether users will trust the output of your system and justify as much as needed
- 90. Make sure there are fallback options
- 92. 1. Know your ML 2. Decide what to optimize for 3. Think about ML early in the product design 4. Collect the right data 5. Think about the user experience Recap How can PM's contribute to ML-driven Products?
- 94. Part-time Product Management Courses in San Francisco, Silicon Valley, Los Angeles, New York, Austin, Boston, Seattle, Chicago, Denver, London, Toronto www.productschool.com
Editor's Notes
- 3. How can PM's contribute to ML-driven Products?
- Classification
- Regression Personalization People also watch Similarity to other videos you’ve watched
- Unsupervised How precise is a sentiment detector? >90% Emotion recognition? 80%
- Object Sequences Reinforcement
- Reinforcement
- Gordon Moore in 1965. He noticed that the number of transistors per square inch on integrated circuits had doubled every year since their invention
- Source: http://chicagoanalyticsgroup.com/blog/december-20th-2016
- Source: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwj_66X77MfUAhXDGT4KHUCHCNwQjRwIBw&url=https%3A%2F%2Fwww.slideshare.net%2Fnervanasys%2Fsd-meetup-12215&psig=AFQjCNH7MBcuLVfA3_c5XYVng34TjnxBFA&ust=1497890998108836
- Source: http://teqmine.com/teqmine-completes-half-million-euro-early-stage-growth-funding/
- The broader concept of machines being able to carry out tasks in a way that we would consider “smart” Robot climbing stairs
- A current application of AI, based around the idea that we should be able to give machines access to data and let them learn for themselves
- You can do potentially very complicated targets for regression if you have data.
- An embedding is a mathematical description of the context for an example. It’s just a vector of floats, but those are calculated (trained) to be the most useful representation for some particular task. In this case, embeddings can be thought of as a point in some high dimensional space. Similar drinks are close together, and dissimilar drinks are far apart.
- An embedding is a mathematical description of the context for an example. It’s just a vector of floats, but those are calculated (trained) to be the most useful representation for some particular task. In this case, embeddings can be thought of as a point in some high dimensional space. Similar drinks are close together, and dissimilar drinks are far apart.
- An embedding is a mathematical description of the context for an example. It’s just a vector of floats, but those are calculated (trained) to be the most useful representation for some particular task. In this case, embeddings can be thought of as a point in some high dimensional space. Similar drinks are close together, and dissimilar drinks are far apart.
- a continuous result
- Probabilities are common regression targets -- think the probability of clicking on something.
- You can do potentially very complicated targets for regression if you have data.
- Tasks where the history of a time series is used to predict the next point. Applications include stock market prediction, weather forecasting, object tracking, disaster prediction
- You can do potentially very complicated targets for regression if you have data.
- mimic that behavior on new examples.
- \
- Unsupervised learning comes into play when you don’t have labeled examples. Why would you do unsupervised clustering? Perhaps to learn about patterns in your data, or to find similar items to investigate further. Think data mining. Clustering in google news (grouping articles that are similar together) Glasses Belt
- \
- Semi-supervised learning is in between: you have some labeled data to train on, but (usually much more) unlabeled data as well. If you can combine those, you can scale much more. This is often a sweet spot for machine learning. Labeled examples can be expensive or difficult to come by, so combining them with unlabeled data (which we often have vast amounts of) can be very powerful.
- Emotion dedection? Most common algorithms perform better than 80%
- An embedding is a mathematical description of the context for an example. It’s just a vector of floats, but those are calculated (trained) to be the most useful representation for some particular task. In this case, embeddings can be thought of as a point in some high dimensional space. Similar drinks are close together, and dissimilar drinks are far apart.
- An embedding is a mathematical description of the context for an example. It’s just a vector of floats, but those are calculated (trained) to be the most useful representation for some particular task. In this case, embeddings can be thought of as a point in some high dimensional space. Similar drinks are close together, and dissimilar drinks are far apart.
- Sometimes your data isn’t linearly separable, so you have to use a nonlinear classifier.
- Think about how our brain works.
- Link = how much a feature is relevant to a certain clasisfication
- Rule is to get from Yellow green without touching the black parts Color = 1 White = 0 Black = -3 Self driving cars Games
- Identify what you’re trying to solve Classification Regression Pick the algorithm Supervised / unsupervised Linear / Nonlinear (Neural net) Get data Training set Validation set Testing set Train and test
- Ctr vs Watchtime
- 5 out of 100 are wrong - precision? 1 out of 100 are spam - no classifier is better Be better than the next best case.
- photo apps existed before. Photo search usually worked by manually tagging pictures, Enter face recognition, object detection, image classification, et al
- Machine Learning is mimicry, not magic. It can only do as well as the data you give it.
- Swipe - a positive or negative feedback? No clicking at all?
- ML/AI features confusing, creepy, or scary. consider how your users may react to new ways the product behaves. think about how to explain their behavior to users. This explanation doesn’t need to be 100% technically accurate.
- Justify as much as possible
- chatbot that responds to tweets. Policy = blacklist of words/ topics
- A PMs job is to figure out what user problems their product needs to solve, and what the product needs to do to provide a solution. ML/AI is not a product, it is just a set of technologies that enable features/products. There are plenty of ways PMs can contribute to ML-driven products, but knowing machine learning algorithms is not one of them.