BC MACHINE LEARNING

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Santiago
@svpino 5 years, 8 months ago 1237 views

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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: 🧵👇

[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.

👇

[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.

👇

[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!

👇

[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?

👇

[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.

👇

[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.

👇

[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
@svpino

10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB

2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5

3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB

4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm

Santiago
@svpino

The 8-step quick-start guide to learn Machine Learning.

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

👇

2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

👇

4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

👇

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Santiago
@svpino

My recommendations to start with machine learning math:

• Statistics 110: Probability — Harvard University
• Essence of Linear Algebra — 3Blue1Brown
• Multivariate Calculus — Coursera

Links and 3 additional free resources here: https://t.co/fEmpmRKEvJ

If you are looking for more machine learning content, give me a follow and I’ll keep your feed interesting 😉.

Santiago
@svpino

11 ways ChatGPT saves me hours of work every day, and why you'll never outcompete those who use AI effectively.

A list for those who write code:

1 of 16

1. Explaining code

Take some code you want to understand and ask ChatGPT to explain it.

I've found explanations are very detailed. This is much quicker than trying to figure out convoluted code.

2 of 16

2. Improve existing code

Ask ChatGPT to improve existing code by describing what you want to accomplish.

It will give you instructions about how to do it, including the modified code.

3 of 16

3. Rewriting code using the correct style

This is great when refactoring code written by non-native Python developers who used a different naming convention.

Notice how ChatGPT not only gives you the updated code; it also explains the reason for the changes.

4 of 16

4. Rewriting code using idiomatic constructs

Very helpful when reviewing and refactoring code written by non-native Python developers.

ChatGPT knows the "Pythonic" way, and it will give you suggestions to improve your code and make it much more readable.

5 of 16

Santiago
@svpino

Beginners guide to start with machine learning.

What you need:

1. A programming language
2. A place to write and run code
3. A way to deal with data
4. A way to visualize results
5. A cool algorithm
6. An introductory course
7. A project to solve

Here are my recommendations.

Most people recommend Python to start.

I do too.

You can do machine learning with many different languages, but today, Python is the best option.

My advice: Get comfortable writing code before looking into machine learning.

You don't have to be a great developer to start.

But if you are, it helps.

Everything you know about building software is helpful to build machine learning systems.

Good developers have the odds in their favor.

"How much Python do I need to know before starting?"

If you ask, you aren't ready yet.

Ideal scenario: You should be comfortable building software. Most people get here within a year.

Let's move on to the 2nd item from the list: A place to write and run your code.

Get familiar with Google Colab.

• It's free.
• Requires no setup to start.
• It's available from anywhere.

Alternative: Kaggle.

More from Machine learning

Quant@Python
@BeingHorizontal

Thread for beginners; How to learn machine learning.

Let’s start with something really simple and useful not just for Machine Learning.

Start with a Python library called pandas. It’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)

Cory Doctorow #BLM
@doctorow

We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

1/

People who hold disfavored views struggle to find one another and mobilize. To find other people that feel the same way as you and make common cause with them to effect political change, you have to reveal your views and suffer social sanction.

2/

Search allows people who hold these views to find one another. If you have a deep feeling about your gender being nonbinary but don't know the words for it, you can search for communities of people who have those words, join them, and discover who you've been all along.

3/

This is why, in this moment, so many ideas are migrating from the fringe to the center: ideas about racial justice, gender identity, alternatives to market systems, etc. People have harbored these views all along, but have held back on expressing them.

4/

Being able to express yourself in private, among people who share your views, is a prelude to going public and putting your case to the wider world in hopes of effecting change.

5/

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Santiago
@svpino

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.

😜

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

👇

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

👇

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."

👇

John Burn-Murdoch
@jburnmurdoch

Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN

For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5

Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN

So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?

Cassie Kozyrkov
@quaesita

1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: https://t.co/dG4l6vPFBT
Spanish version: https://t.co/gFAjPQ5clS

#AI #MachineLearning #Statistics #RStats

2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.

3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*

4/🧵 In other words, start with a pilot phase where the objective isn't finding answers, it's finding a good approach to finding answers.

5/🧵 That means you're encouraged (ENCOURAGED!) to start with everything your stats classes told you not to do:

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Richard James
@RJSHutton

I think a plausible explanation is that whatever Corbyn says or does, his critics will denounce - no matter how much hypocrisy it necessitates.

One of the oddest features of the Labour tax row is how raising allowances, which the media allowed the LDs to describe as progressive (in spite of evidence to contrary) through the coalition years, is now seen by everyone as very right wing
— Tom Clark (@prospect_clark) November 2, 2018

Corbyn opposes the exploitation of foreign sweatshop-workers - Labour MPs complain he's like Nigel

He speaks up in defence of migrants - Labour MPs whinge that he's not listening to the public's very real concerns about immigration:

He's wrong to prioritise Labour Party members over the public:

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$Joey Tawadrous \U0001f1ee\U0001f1ea$

Joey Tawadrous 🇮🇪...
@joeytawadrous

1) Core attributes of a promising founder worth following:
- Progress/trajectory over time
- How well they lead a team &
- Make the vision happen
- Most impressed when someone is way ahead of where you thought they would be when you check up/see them a few months later.

2) Indie Makers have such a benefit of being small, fast, nimble workers in comparison to big companies.

We can easily pivot and vastly change a project of ours. We can change as we learn more about our users.

Big companies can take months/years for even the smallest of pivots.

3) Before you find a product market you can pivot your project as many times as necessary until you find something that works.

Then you only have one thing to do: scale, scale, scale.

4) Be obsessed with the little details.
- listen to every single person who uses your product
- reply to every single person who emails you about your product
- ask them what they like and do not like
- iterate your product, make changes, pivot if necessary

5) Paul Graham's (@paulg) name came up with a feeling of great admiration! Naturally! It was Paul who said:

In the early stages of a company just;
- write code
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Nothing else.

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Sheetal Rijhwani
@RijhwaniSheetal

How you can filter stocks for Intraday trades - 🧵

As a kid, we would do homework before school the next day - you have to do homework here too. A specific sector performs on a particular day and studying things a day before will help you spot that particular sector. (1/11)

To prepare for the next trading day, you got to check the Index charts of all sectors. You got to keep an eye on a specific Index if you see a few factors:

- If any Index closed exactly at the resistance, you can get a trade on either side on the next day. (2/11)

- If it gave a rejection candle at resistance.

- If any Index closed exactly at support, you can get a trade on either side the following day.

- If there was a bullish signal at the support.

- If any Index gave a breakout or breakdown and then gave a good closing. (3/11)

- If any Index has taken support at 50 or 200 EMA.

Once you spot the Index, look at all the stocks in that particular Index. Pick at least 3-4 stocks from that Index and add them to your watchlist for the next day. It's ideal to check both higher and lower timeframes. (4/11)

What you should do next day? (When I am unable to check Indices and stocks a day before, I sometimes directly start with this process.)

- Start with checking the top gainers/losers list. I observe market from 9:15 to 9:45am to check the sectors performing well on that day.(5/11)

Bartosz Ziembinski
@zmbnski

Still wondering about this 🤔

I am considering adding a 'compose' feature to @buzzwaysapp

It would enable a user to compose a thread out of different tweets and submit it to Buzzways.

What do you guys think?
— Bartosz Ziembinski (@zmbnski) November 23, 2018

save as q

Vibhu Vashisth
@VIBHU_Tweet

Surkanda devi mandir is a famous shrine of Hindus located near Kaddukhal distt of Uttarakhand, 11 Km from the town of Dhanaulti and 35 Km from Mussoorie.
The Temple is one of the 51 Shakti Peethas spread all over Indian Subcontinent and is dedicated to Goddess Surkanda.

Nestled on a Hill at an Altitude of 2700 m approx, the place offers a splendid view of the snow capped mountains, the picturesque scenic beauty that offers an experience of the lifetime.
The Temple build in the traditional Garhwal style exuberates the Architectural splendour.

The origins of the Surkanda Mata Mandir is attributed to Goddess Sati, consort of Bhagwan Shiv who in a rage immolated herself in her arrogant Father Daksha's Yajna Kund bcoz he didn't invite Bhagwan Shiv in the Yajna.

Devastated by the death of Sati Shiva wandered all over the cosmos performing Tandava-The Dance of Cosmic Destruction.Fearing the total annihilation of cosmos, Gods requested Bhagwan Vishnu to pacify Shiva.Bhagwan Vishnu then used his discuss to destroy the burning corps of Sati.

The pieces of Sati Mata's body then got scattered all over the region which were later established as Shakti Peethas by Bhagwan Shiva himself.Its believed that the head of Sati fell here and it was called Sirkhanda Mata Temple before, but with passage of time it became Surkanda.