BC MACHINE LEARNING
Saved by @Alex1Powell

11 key concepts of Machine Learning.

β€” Supervised Learning Edition β€”

πŸ§΅πŸ‘‡

 😜

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.

😜
1. Labels

(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

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2. Features

(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

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3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

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4. Labeled sample

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)
5. Unlabeled sample

Unlabeled samples contain features, but they don't contain the label: (x, ?)

We usually use a model to predict the labels of unlabeled samples.

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6. Model

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.

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7. Training

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.

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8. Validation

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.

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9. Inference

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.

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10. Regression

A regression model predicts continuous values, for example:

- the value of a house
- the price of a stock
- tomorrow's temperature

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11. Classification

A classification model predicts discrete values, for example:

- the picture is showing a dog or a cat
- the message is spam or not
- the forecast is sunny or overcast

More from Santiago

Santiago
Santiago
@svpino
10 machine learning questions you should be able to answer.

I started experimenting with a questionnaire before interviewing candidates. Applicants submit a video or written answers to apply for the position.

Thread: Let's look at 10 of these questions.

The questionnaire solves a couple of problems:

1. Weeds out candidates that aren't ready for the job.

2. It significantly cuts down the time we spend talking synchronously.

One problem: It requires extra time from the candidate before the phone interview.

Here are some numbers on a job posting before using the questionnaire:

β€’ 249 applicants
β€’ 13 good applications

That's only 5%.

This sucks.

Numbers after using the questionnaire:

β€’ 44 applicants
β€’ 21 good applications

That's 47%.

This is much better.

I look at it this way:

Sooner or later, I'll ask these questions, and the candidate will have to answer them.

I'd rather give them space and the opportunity to think about their answers without any pressure.

Those who can't or don't want to answer won't apply. That's fine.
DS
Santiago
Santiago
@svpino
For a long time, I didn't understand how to use Virtual Environments in Python 🐍.

If this is just, let's end it here and now: πŸ§΅πŸ‘‡

[2] Virtual Environments let you deal with the dependencies that your code has with external Python libraries.

It avoids having conflicts when your projects depend on different versions of the same library.

πŸ‘‡

[3] Let's imagine that you are building your first Python project and you install the "requests" library:

pip install requests

You get version 2.24.0 installed in your system.

πŸ‘‡

[4] A month later, you decide to work on your second project. It also needs the "requests" library.

But the latest version is not 2.24.0 anymore.

Now version 3 is available, and that's the one you want to use!

πŸ‘‡

[5] You could upgrade your entire system to version 3, but then you'll be potentially breaking the first project you built that depends on 2.24.0!

Can you imagine this happening on a server with many more applications running?

πŸ‘‡
MACHINE LEARNING
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Santiago
Santiago
@svpino
What's machine learning?

Not a framework. Not a different language. Not the latest "cool" thing around the Internet.

Thread: Let me convince you that machine learning is the best thing since sliced bread 🍞.

Let's start with a simple exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function. It should be simple, right?

To avoid using a specific syntax, let's represent this function as follows:

y = f(X)

Where:
β€’ X β†’ The input number
β€’ y β†’ The result (0 if even, 1 otherwise)
β€’ f β†’ The function that does the work

We can now get to the interesting part.

If you are a programmer, writing this function is simple.

You know the relationship between X (the input number) and y (the output), so you can easily write the rules (the function.)

In machine learning, we don't write the rules. We learn them.

Imagine we had an algorithm that processes many samples (X β†’ y), learns the relationship between them, and comes up with the correct rules.

We call these samples "training data," and they would like like this:

β€’ 2 β†’ 0
β€’ 5 β†’ 1
β€’ 8 β†’ 0
β€’ 4 β†’ 0
β€’ 3 β†’ 1
β€’ ...
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Santiago
Santiago
@svpino
Some of my machine learning books.


Hands-On Machine Learning from @aureliengeron and Deep Learning with Python from @fchollet.
ALL
Santiago \U0001f1fa\U0001f1f8\U0001f1fa\U0001f1e6
Santiago πŸ‡ΊπŸ‡ΈπŸ‡ΊπŸ‡¦...
@svpino
You are building a binary classification model to determine whether a picture shows a hot dog or not.

Which of the following activation functions is a good candidate for the output layer?

1. Rectifier Linear Unit (ReLU)
2. Leaky ReLU
3. Sigmoid
4. Softmax

Think about it.

The answer:

You could use either Sigmoid or Softmax.

I'm sure most of you picked Sigmoid. It's certainly the best way to approach this problem.

But Softmax is also an option β€”although unconventional for a binary classifier.

How about the other choices?

ReLU returns its input if positive, zero otherwise. Leaky ReLU is based on ReLU but with a slight slope for negative values.

ReLU and Leaky ReLU will always give us the previous layer output with no modifications if the input is always positive.

This doesn't help us.

Sigmoid has the ability to squash its input into a range that goes from 0 to 1.

This is good because we can now set a specific threshold for the output of a binary classifier.

Here is an example.

Any image with an output equal to or under 0.5 doesn't show a hot dog. Any image with an output above 0.5 shows a hot dog.

Sigmoid makes it very easy.

How about Softmax?
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Machine learning terms you must know about as a beginner.

πŸ§΅πŸ‘‡

These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

(2 / 11)

In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.

(3 / 11)


In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.

(4 / 11)


Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

(5 / 11)
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Are you planning to learn to Python for machine learning this year?

Here's everything you need to get started.
πŸ§΅πŸ‘‡

In this thread we'll look at all the concepts in Python you need to know for machine learning along with free resources to help you out.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19

You can use many languages for machine learning, why Python?

Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)

These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)

Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)
MACHINE LEARNING , PYTHONML
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Caio Rolla
Caio Rolla
@caio_rolla
Starting a new project using #Angular? Here is a list of all the stuff i use to launch my projects the fastest i can.

A THREAD πŸ‘‡

Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using https://t.co/aY5llDtXg8.

I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.

Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.

There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.

There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics

Are you not using custom schematics yet? Take a look at this:

https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C

You can automate all the boilerplate with hundreds of files associates with creating a new feature.
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
These are the tools you will need for machine learning in Python.

πŸ§΅πŸ‘‡

Anaconda

When you work in python, you'll be working with several frameworks and many of them work only on specific versions of python.

(2 / 13)

Now imagine downloading a new version of python and then installing it for every framework you want to work with 😬.

Meet Anaconda which allows you to run several versions of python. It comes pre-installed with several data science and machine learning frameworks.

(3 / 13)

Pip-env is also a way of maintaining several versions of Python and comes pre installed with Python.
You can use pip env or Anaconda, whichever works for you.

(4 / 13)

Jupyter Notebooks

Jupyter notebooks is an IDE just like VS code or Sublime. The special thing about jupyter is that you can parts of code in mini code editors called cells. This is great for prototyping and testing code.

(5 / 13)
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Here's everything you need to know about the math for machine learning.

(+ free resources )
πŸ§΅πŸ‘‡

Before diving into the math, I suggest first having solid programming skills.

For exampleπŸ‘‡

(2 / 18)

In Python, these are the concepts which you must know:

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

(3 / 18)

If you want to learn those concepts for python, these courses are freecodecamp could be of help to you.

πŸ”—Basics:youtubeβˆ™com/watch?v=rfscVS0vtbw
πŸ”—Intermediate :youtubeβˆ™com/watch?v=HGOBQPFzWKo

(4 / 18)


πŸ‘‰You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 18)
MACHINE LEARNING

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Further Examination of the Motif near PRRA Reveals Close Structural Similarity to the SEB Superantigen as well as Sequence Similarities to Neurotoxins and a Viral SAg.

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Most software founders, particularly in B2B, need to get radically better at sales. @Steli , who is the best person for explaining the scrappy stages in the beginning and then building out a team with scripts and processes, wrote a guide to:

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An interesting set of games that I've been vaguely interested in since I was a kid, after seeing one at a friend's house and never being able to find it again, was the BATTLES IN A DISTANT DESERT/BATTLES ON DISTANT PLANETS series.


These are turn-based wargames for windows 3.x, sharing the same engine.
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and Battles on Distant Planets is from 1991, and takes place in SPACE!
I'm pretty sure this is the one I played as a kid.


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* Player vs. Player
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* Computer vs. Computer

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