BC MACHINE LEARNING

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Santiago
@svpino 5 years, 5 months ago 1172 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

I told everyone that I didn't care.

"Screw math! I've never been great with it, so I'm not starting with machine learning to fail at the end."

That was many years ago.

Math is still hard, but I don't think you should be scared at all. Here is why: 🧵👇

[1] One thing changed my mind: school pushed me to the deep end of the pool, and I was forced to swim.

I had to face my fears, and I started machine learning and realized that the math involved is not as scary (or as much) as some people believe.

👇

[2] Probably one of the most frequent questions I get is around the math needed for machine learning.

Answer:

▫️ Probabilities and Statistics
▫️ Linear Algebra
▫️ Calculus

But it turns out that this is not helpful.

👇

[3] What are you gonna do with this?

Are you going to learn all there's is about linear algebra before looking into machine learning?

How about probabilities and statistics? Or calculus? Do you know how extensive calculus is?

👇

[4] Many people stop looking into machine learning as soon as somebody tells them that calculus is a pre-requisite.

First of all, the amount of calculus you truly need is minuscule.

Second, without proper direction, there's no way you'll find your way through the noise.

👇

Santiago
@svpino

One of the most popular activation functions used in deep learning models is ReLU.

I asked: "Is ReLU continuous and differentiable?"

Surprisingly, a lot of people were confused about this.

Let's break this down step by step: ↓

Let's start by defining ReLU:

f(x) = max(0, x)

In English: if x <= 0, the function will return 0. Otherwise, the function will return x.

If you draw this function, you'll get the attached chart.

Notice there are no discontinuities in the function.

This should be enough to answer half of the original question: the ReLU function is continuous.

Let's now think about the differentiable part.

A necessary condition for a function to be differentiable: it must be continuous.

ReLU is continuous. That's good, but not enough.

Its derivative should also exist for every individual point.

Here is where things get interesting.

We can compute the derivative of a function using the attached formula.

(I'm not going to explain where this is coming from; you can trust me on this one.)

We can use this formula to see whether ReLU is differentiable.

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Santiago
@svpino

20 machine learning questions that will make you think.

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

🧵👇

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?

👇

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?

👇

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.

👇

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?

👇

Santiago
@svpino

Some of the things I've learned in more than 20 years in the tech industry.

You need to hear these.

🧵👇

Listen to more people who don't look like you, don't speak your language, and don't come from the same place you do.

We aren't doing this near enough.

Small habits compound.

No small improvement is too small.

Just aim for something new every day, and you'll be surprised at the end.

Take responsibility.

It doesn't really matter how you feel, I'm sure you could have done better.

Always keep the big picture in mind and never lose the forest for the trees.

Be the person that pulls everyone out of the rabbit holes.

Santiago
@svpino

I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."

This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.

More from Machine learning

Pratham Prasoon
@PrasoonPratham

Are you planning to learn to Python for machine learning this year?

Here's everything you need to get started.
🧵👇

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)

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 👇

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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Santiago
@svpino

20 machine learning questions that will make you think.

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

🧵👇

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?

👇

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?

👇

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.

👇

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?

👇

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:

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)

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Hello Dear Tweeples!

It’s Siddhars time of the week! Last week I shared story of Guru (Machamuni or Yogi Matsyendranath), this week I bring you information on his Shishya.

#Thread on Gorakanathar Siddhar.

@LostTemple7

His origin story is interesting, though I couldn’t verify it’s authenticity, so not sharing the story here. He’s shishya of Machamuni Siddhar and also learnt from Agasthiya Siddhar.

@HelloNNewman @RajiIndustani @Kishoreciyer1

Unlike other Siddhars, he is depicted as either with a Tiger or seated & wearing Tiger skin. While I couldn’t find any explanation for this. My interpretation is as follows:

Tiger is a predator that lurks around and attacks unexpectedly.

Our 5 indriyas and 5 vikitis also similar to Tiger, they lurk around and attacks our thoughts when we’re least expecting. Gorakkar is said to have mastered these aspects, thus to denote this, he shown with Tiger.

@Itishree001 @VarierSangitha @jananisampath

Gorakanathar or Gorakshnath (as he’s known in the Nath Sampraday) is celebrated Guru in both Northern and Southern part of India. He’s one of the NavNath in Nath Sampraday and also 18 Siddhars of Tamil tradition. During his life, he’s travelled Indian subcontinent extensively +

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Lu_lu💘
@luciame16

Some thoughts on episode 54 of #Hercai

If you ever cross the distance and come to me, come with love that blooms in your heart.
~ Şems-i Tebrizi

#Reyyan #Miran #ReyMir #EbruŞahin #AkınAkınözü
#HercaiThoughts

1
I have to make a note before writing about this episode. I was the person (still am) who thinks that bringing back D. was a bad idea. I repeat it that for me the writing of S1 and S2 is like tossed into the bin with that plot evolution, but they had to invent something+
#Hercai

2
+ to be able to continue to prolong the general story after Aslan’s story did not work out. All the suffering of M., all the crying seems for “nothing” for two seasons and we viewers are also kind of fed up in seeing him cry all the time when with the upcoming episodes+
#Hercai

3
+he is justified the most to cry. With her resurrection now the attention shifts back to Miran’s storyline and I don’t know if I like this to be honest. This said, Dilsha is here and we have to deal with her.
#Hercai

4
So, my comments are going to be how I find they wrote her into the story, the details we get to know, the flashbacks we get to see, the way it was filmed and directed, with leaving out if I agree on it or not but describing along the way what I saw.
#Hercai

Andrew Chen
@andrewchen

👇 Thread. New deck getting published this week: "Consumer startups are awesome, and here's what I'm looking to invest in at Andreessen Horowitz." If you want to read it, subscribe to my newsletter here: https://t.co/262t8eh0wf

1/ A lot of new consumer technologies have been introduced to US households in the last 100 years. But it's taken many of them - like the telephone - more than 50 years to get to the majority of the US. Why is that?

2/ We had to literally teach people how to use phone. Which end goes to your mouth, which goes to your ear. Say "hello" when people call. The motivation of consumers to talk to their friends has always been there, but we had to teach the behavior

3/ If you compare phones to the latest technologies, there's been a huge shift. Things are being picked up much faster.

4/ Even while there's been all this innovation recently, physically speaking, we are still the same human beings from 100,000 years ago.

$\uc2e0\ub150\uc758\uc0ac\ub098\uc774$

신념의사나이...
@Belposto_korea

What does Gold- Backed mean?
The Chinese Elders have been accumulating gold for hundreds of years. They are gold-rich. Some of their gold was loaned to the Central Banking System so that it could fund the rebuilding of Europe after the many war-torn years from World Wars I and II

BTW they never returned it but used it to start their financial empire to enslave humanity. This is the Central Bank Banking System./2

Maybe this is why one of the conditions to use their gold for the QFS is the demise and full disclosure of the Central Bank banking system before the RV can take place./3

The Elders have caves of hidden gold and are now using it to gold-back the currencies in the QFS for every country in the world. It is important to understand that the QFS and the money it is about to release is all brand-new currency./4

It was designed by the Alliance to fund the new financial system that will reset all Currencies of the world to Gold-Backed currency./5