Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)
And if you don't follow me, well, you just hurt my feelings.
😜
11 key concepts of Machine Learning.
— Supervised Learning Edition —
🧵👇
Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)
And if you don't follow me, well, you just hurt my feelings.
😜
(Also referred to as "y")
The label is the piece of information that we are predicting.
For example:
- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not
👇
(Also referred to as "x")
These are the input variables to our problem. We use these features to predict the "label."
For example:
- pixels of a picture
- number of bedrooms of a house
- square footage of a house
👇
(This is also known as "examples.")
A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."
👇
Labeled samples are used to train and validate the model. These are usually represented as (x, y), where "x" is a vector containing all the features, and "y" is the corresponding label.
For example, a labeled sample could be:
([3, 2, 1500], 350000)
Unlabeled samples contain features, but they don't contain the label: (x, ?)
We usually use a model to predict the labels of unlabeled samples.
👇
A model defines the relationship between features and the label.
You can think of a model as a set of rules that, given certain features, determines the corresponding label.
For example, given the # of bedrooms, bathrooms, and square footage, we get the price.
👇
Training is a process that builds a model.
We show the model labeled samples during training and allow the model to gradually learn the relationships between features and the label.
👇
Validation is the process that lets us know whether a model is any good.
Usually, we run a set of (unseen) labeled samples through a model to ensure that it can predict the labels.
👇
Inference is the process of applying a trained model to unlabeled samples to obtain the corresponding labels.
In other words, "inference" is the process of making predictions using a model.
👇
More from Santiago
More from Machine learning
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... 👇🧵
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
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 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ 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
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
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 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
You May Also Like
“We don’t negotiate salaries” is a negotiation tactic.
Always. No, your company is not an exception.
A tactic I don’t appreciate at all because of how unfairly it penalizes low-leverage, junior employees, and those loyal enough not to question it, but that’s negotiation for you after all. Weaponized information asymmetry.
Listen to Aditya
And by the way, you should never be worried that an offer would be withdrawn if you politely negotiate.
I have seen this happen *extremely* rarely, mostly to women, and anyway is a giant red flag. It suggests you probably didn’t want to work there.
You wish there was no negotiating so it would all be more fair? I feel you, but it’s not happening.
Instead, negotiate hard, use your privilege, and then go and share numbers with your underrepresented and underpaid colleagues. […]
Always. No, your company is not an exception.
A tactic I don’t appreciate at all because of how unfairly it penalizes low-leverage, junior employees, and those loyal enough not to question it, but that’s negotiation for you after all. Weaponized information asymmetry.
Listen to Aditya
"we don't negotiate salaries" really means "we'd prefer to negotiate massive signing bonuses and equity grants, but we'll negotiate salary if you REALLY insist" https://t.co/80k7nWAMoK
— Aditya Mukerjee, the Otterrific \U0001f3f3\ufe0f\u200d\U0001f308 (@chimeracoder) December 4, 2018
And by the way, you should never be worried that an offer would be withdrawn if you politely negotiate.
I have seen this happen *extremely* rarely, mostly to women, and anyway is a giant red flag. It suggests you probably didn’t want to work there.
You wish there was no negotiating so it would all be more fair? I feel you, but it’s not happening.
Instead, negotiate hard, use your privilege, and then go and share numbers with your underrepresented and underpaid colleagues. […]
https://t.co/6cRR2B3jBE
Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.
https://t.co/FBfXhUrH5d
Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.
...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.
Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.
https://t.co/FBfXhUrH5d
Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.
...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.
Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
