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This is a Twitter series on #FoundationsOfML.
❓ Today let's look at two fundamental modelling paradigms that are used throughout the whole ML landscape.
Let's dive into Generative vs Discriminative models...
👇🧵 1/20
❓ Today let's look at two fundamental modelling paradigms that are used throughout the whole ML landscape.
Let's dive into Generative vs Discriminative models...
👇🧵 1/20
Say we want to learn to recognize 🐶dogs and 😺cats.
Let's not worry about input format right now and instead think in terms of abstract features, like, does it have pointy ears?
There are at least two ways in which we can do it.
👇 2/20
Let's not worry about input format right now and instead think in terms of abstract features, like, does it have pointy ears?
There are at least two ways in which we can do it.
👇 2/20
1️⃣ We can try to learn what is a dog and what is a cat, *independently*.
That is, which are the fundamental characteristics that best *define* each one of those classes.
👇 3/20
That is, which are the fundamental characteristics that best *define* each one of those classes.
👇 3/20
This is a Twitter series on #FoundationsOfML. Today, I want to talk about another fundamental question:
❓ What makes a metric useful for Machine Learning?
Let's take a look at some common evaluation metrics and their most important caveats... 👇🧵
❓ What makes a metric useful for Machine Learning?
Let's take a look at some common evaluation metrics and their most important caveats... 👇🧵
Remember our purpose is to find some optimal program P for solving a task T, by maximizing a performance metric M using some experience E.
We've already discussed different modeling paradigms and different types of experiences.
👉 But arguably, the most difficult design decision in any ML process is which evaluation metric(s) to use.
👉 But arguably, the most difficult design decision in any ML process is which evaluation metric(s) to use.
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric M.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric M.
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
❓ What is Machine Learning?
This is my preferred way of explaining it... 👇🧵
❓ What is Machine Learning?
This is my preferred way of explaining it... 👇🧵
Machine Learning is a computational approach to problem-solving with four key ingredients:
1️⃣ A task to solve T
2️⃣ A performance metric M
3️⃣ A computer program P
4️⃣ A source of experience E
1️⃣ A task to solve T
2️⃣ A performance metric M
3️⃣ A computer program P
4️⃣ A source of experience E
You have a Machine Learning solution when:
🔑 The performance of program P at task T, as measured by M, improves with access to the experience E.
That's it.
Now let's unpack it with a simple example 👇
🔑 The performance of program P at task T, as measured by M, improves with access to the experience E.
That's it.
Now let's unpack it with a simple example 👇
