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.
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?
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
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
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
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Hinweis der Redaktion
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
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.
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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
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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.