BC MACHINE LEARNING
Saved by @CodyyyGardner

For a long time, I didn't understand how to use Virtual Environments in Python šŸ.

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

 [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.

šŸ‘‡
[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.

šŸ‘‡
[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!

šŸ‘‡
[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?

šŸ‘‡
[6] Virtual environments solve this problem.

The first step for every new project is to create a virtual environment for it.

Some people have a central location where they store all environments. I prefer to keep them inside the project folder.

šŸ‘‡
[7] You can create a new virtual environment with Python 3 using the following command:

python3 -m venv .myvenv

Then, you can use "source" to activate the environment.

At this point, you'll have full isolation for your project.

šŸ‘‡
[8] If you install any libraries within a virtual environment, they will never mess with the libraries installed at the system level or other virtual environments.

And this is great!

Here is a @realpython's article covering virtual environments: https://t.co/lgXqJDUlKw
[9] The built-in "venv" module is not the only way to create virtual environments. Here are other options:

- conda
- pipenv
- virtualenv

What's your choice?

More from Santiago

Santiago
Santiago
@svpino
A surprising situation.

A machine learning model predicting 3 classes ends up with these results:

ā€¢ Class A: 90% accuracy
ā€¢ Class B: 80% accuracy
ā€¢ Class C: 70% accuracy

Class C seems to be the worst-performing.

But you know already that I'll say it isn't. ā†“

Here is the correct answer: We don't know yet which class is the worst.

This is important to understand, so keep reading.

Many people make this same mistake every day and try to fix things that ain't broken.

We are missing something, so let's go and get it.

How about if we ask a friend to look at the dataset manually?

Let's say these are the results of our friend doing the job:

ā€¢ Class A: 95% accuracy
ā€¢ Class B: 100% accuracy
ā€¢ Class C: 71% accuracy

Do you see what just happened?

The model is actually really accurate when predicting Class C! 70% versus 71% when done by a human.

Class B, however, is a disaster: 80% versus 100% when done manually.

Class B seems to be the worse performing class!

The work that our friend did is called a "baseline."

Without having it, it's hard to determine how good are the results of a model.

I've seen tons of money down a drain just because teams didn't create a baseline beforehand.

But now, that won't happen to you šŸ˜‰.
ALL
Santiago
Santiago
@svpino
Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

ā†“

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset ā†’ Model.

This is great, but not enough.

Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem ā†’ Potential Solution ā†’ Data.
ALL
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Santiago
Santiago
@svpino
20 machine learning questions that will make you think.

(Cool questions. Not the regular, introductory stuff that you find everywhere.)

šŸ§µšŸ‘‡

1. Why is it important to introduce non-linearities in a neural network?

2. What are the differences between a multi-class classification problem and a multi-label classification problem?

3. Why does the use of Dropout work as a regularizer?

šŸ‘‡

4. Why you shouldn't use a softmax output activation function in a multi-label classification problem when using a one-hot-encoded target?

5. Does the use of Dropout in your model slow down or speed up the training process? Why?

šŸ‘‡

6. In a Linear or Logistic Regression problem, do all Gradient Descent algorithms lead to the same model, provided you let them run long enough?

7. Explain the difference between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent.

šŸ‘‡

8. What are the advantages of Convolution Neural Networks (CNN) over a fully connected network for image classification?

9. What are the advantages of Recurrent Neural Networks (RNN) over a fully connected network when working with text data?

šŸ‘‡
ALL , MACHINE LEARNING
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 šŸž.

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:
ā€¢ X ā†’ The input number
ā€¢ y ā†’ The result (0 if even, 1 otherwise)
ā€¢ f ā†’ 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 ā†’ 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:

ā€¢ 2 ā†’ 0
ā€¢ 5 ā†’ 1
ā€¢ 8 ā†’ 0
ā€¢ 4 ā†’ 0
ā€¢ 3 ā†’ 1
ā€¢ ...
ALL
Santiago
Santiago
@svpino
You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below šŸ‘‡

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through


You can get the answer using Bayes' theorem, but let's try to come up with it in a different ā€”maybe more intuitiveā€” way.

šŸ‘‡


Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

šŸ‘‡

Let's now test both groups, starting with the 100 people sick:

ā–«ļø 99 of them will be diagnosed (correctly) as sick (99%)

ā–«ļø 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

šŸ‘‡
HEALTH

More from Machine learning

Alejandro Piad Morffis
Alejandro Piad Morffis
@AlejandroPiad
This is a Twitter series on #FoundationsOfML.

ā“ 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... šŸ‘‡šŸ§µ

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 1ļøāƒ£ Supervised and 2ļøāƒ£ Unsupervised Learning šŸ‘‡

1ļøāƒ£ 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.

šŸ‘‰ 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.
MACHINE LEARNING
emmi
emmi
@emmibevensee
The "polarization is the problem" narrative to me is bad because it implies the status quo was a stable and acceptable set of trade-offs. It's the equivalent of a pollyanna "why can't we all get along" but the we in question is white supremacy and its targets.

like i understand that conflict mediation requires mutual concessions (i did my MA in conflict studies) and while it's important to meet people's deep needs across spectrums of difference, certain things are just not up for negotiation.

Also while peacebuilding work is good. It's not just "seeing each other as human" it's literally about transforming structural issues. (hence Galtung's distinction between positive and negative peace wherin negative peace is like a ceasefire and positive is a transformed society.

And I think that to whatever extent that kind of peacebuilding work is applicable, it's not between fascists and people who think fascists are bad. Fascists are not invited. They are only invited if they seize territories and can't be crushed. Then secret negotiations.

The US is always like "we don't negotiate with terrorists" but literally every president negotiates with who they consider to be terrorists. It's all just down low. But that's just because counter-insurgency failed.
MACHINE LEARNING
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Quant@Python
Quant@Python
@BeingHorizontal
Thread for beginners; How to learn machine learning.

Letā€™s start with something really simple and useful not just for Machine Learning.

Start with a Python library called pandas. Itā€™s a huge library but it's mostly fun for traders because it's kind of rewarding to learn each function and how it gonna affect the way you handle the financial data.

Then learn some basic stuff about Python library called NumPy. Pandas is built on NumPy. Numpy is huge, you just need to understand some basic NumPy functions like NumPy shapes and NumPy arrays

If you understand pandas then you will understand NumPy arrays and shapes easily. Side note: Numpy shapes gonna play an important part in the deep learning model called LSTM - mainly used for time-series analysis(such as financial data) and Natural Language Processing (NLP)
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.
šŸ§µšŸ‘‡

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
Pratham Prasoon \U0001f680
Pratham Prasoon šŸš€...
@PrasoonPratham
Here's how I got started with my first machine learning project and you can too.
Let's take a look.

(This thread will take from zero to being a hero in machine learning, trust me)

(1 / 22)
šŸ§µšŸ‘‡


Getting started with your first machine learning project might actually much easier than it seems, if I can do it, certainly anyone can.

I did not use:
- Any Math
- An expensive computer
- Complex programming concepts

(2 / 22)

Here's what I did use:

- A free GPU on Google Colab
- Python
- TensorFlow
- Numpy
- Pandas
- Kaggle
- Scikit-Learn
- Google
- StackOverFlow

(3 / 22)

This project was actually a Kaggle challenge based on the MNIST dataset which is a collection images of 70,000 hand written digits.

You can find the dataset herešŸ‘‡
šŸ”—//kaggle.com/c/digit-recognizer

(4 / 22)


Before we go over the code of this project, it is highly reccomended that you complete this free course on YouTubešŸ‘‡

Machine Learning foundations course
šŸ”—//youtu.be/_Z9TRANg4c0

CodešŸ‘‡
šŸ”—//colab.research.google.com/github/PrasoonPratham/Kaggle/blob/main/MNIST.ipynb

(5 / 22)
MACHINE LEARNING

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Brian Cates
Brian Cates
@drawandstrike
Great article from @AsheSchow. I lived thru the 'Satanic Panic' of the 1980's/early 1990's asking myself "Has eveyrbody lost their GODDAMN MINDS?!"


The 3 big things that made the 1980's/early 1990's surreal for me.

1) Satanic Panic - satanism in the day cares ahhhh!

2) "Repressed memory" syndrome

3) Facilitated Communication [FC]

All 3 led to massive abuse.

"Therapists" -and I use the term to describe these quacks loosely - would hypnotize people & convince they they were 'reliving' past memories of Mom & Dad killing babies in Satanic rituals in the basement while they were growing up.

Other 'therapists' would badger kids until they invented stories about watching alligators eat babies dropped into a lake from a hot air balloon. Kids would deny anything happened for hours until the therapist 'broke through' and 'found' the 'truth'.

FC was a movement that started with the claim severely handicapped individuals were able to 'type' legible sentences & communicate if a 'helper' guided their hands over a keyboard.
CULTURE
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Vibhor Varshney
Vibhor Varshney
@nakulvibhor
On the occasion of youtube 20k and Twitter 70k members

A small tribute/gift to members

Screeners

technical screeners - intraday and positional both

before proceeding - i have helped you , can i ask you so that it can help someone else too

thank you

positional one

run - find #stock - draw chart - find levels

1- Stocks closing daily 2% up from 5 days
https://t.co/gTZrYY3Nht

2- Weekly breakout
https://t.co/1f4ahEolYB

3- Breakouts in short term
https://t.co/BI4h0CdgO2

4- Bullish from last 5

intraday screeners

5- 15 minute Stock Breakouts
https://t.co/9eAo82iuNv

6- Intraday Buying seen in the past 15 minutes
https://t.co/XqAJKhLB5G

7- Stocks trading near day's high on 5 min chart with volume BO intraday
https://t.co/flHmm6QXmo

Thank you
SCREENERS , ALL , NAKULSMIX
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Cole Escola
@ColeEscola
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SOCIETY
Conservative Trader
Conservative Trader
@AnandableAnand
How to get rich trading (without getting lucky)

Complete beginnerā€™s guide to wealth of Rs 10 Cr in 10 years

Step 1: start trading live

1/n

Nothing can substitute experience of live trading

No amount of back-test, studies or indicators can substitute training your mind with screen time

As a beginner, In live trading, you will make losses almost everyday

2/n

So here are the conditions which you canā€™t violate in first year
If you canā€™t follow these rules, this document is not for you

Start with trading capital of Rs 1 lakhs

Max loss per trade 0.25%, i.e. 250 Rs

Max loss for the day, 1%, i.e. Rs 1000

Max 6 trades in a day

3/n

I know, these conditions will cripple you in terms of what all you can trade and how

But just like any start up, trading is also about making it through initial gruelling constraints of life

You will also learn, how to turn these adverse rules to your advantage

4/n

Btw, you can sell directional banknifty options, obviously you can buy options, can do cash stock trading and much more under these conditions

Think about it, you will be do all kinds of instruments, just that PNL will not be roller coaster ride or exciting like a casino

5/n
TRADING , ALL