BC MACHINE LEARNING

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Santiago
@svpino 4 years, 5 months ago 976 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

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.

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

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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.

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:

• 249 applicants
• 13 good applications

That's only 5%.

This sucks.

Numbers after using the questionnaire:

• 44 applicants
• 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.

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:
• X → The input number
• y → The result (0 if even, 1 otherwise)
• f → 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 → 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:

• 2 → 0
• 5 → 1
• 8 → 0
• 4 → 0
• 3 → 1
• ...

More from Machine learning

Marcus Mengs
@mame82

Okay, doing my first baby steps with r2frida (which combines the power of @radareorg and @fridadotre).

Gonna share my progress in this thread (live, so keep calm).

The goal: Runtime inspection of data sent out by TikTok !!before!! it gets encrypted

1/many

First of all, we do not start from zero. I got some prior knowledge from past reversing attempts and want to share some important facts.

TikTok's (log data) encryption is accomplished by a native library. The Android Java code just serves as proxy function to the native function

The decompiled code for the respective native JNI function of an older TikTok version looks something like this, but in this example I use the most current TT version (no statical analysis done, yet)

In case you never reversed native libraries which were build to interface with Android Java layer via JNI, I highly suggest the entry level introduction on the topic by @maddiestone

Before we start, I want to pinpoint some important aspects (which are also covered by Maddie's videos).

1) Unlike raw C-functions, JNI functions like the one showcased above, receive pointers to complex Java objects .

F.e. a function receiving a String on the Java layer...

Pratham Prasoon
@PrasoonPratham

Can you get a job in data science and machine learning without a college degree?

🧵👇

Short Answer: Yes.

Long Answer, keep reading 👇

(Advice from industry experts who I talked to.)

Companies are looking for people who add value.
In order to add value, you'll need skills. Simple as that.

Get the skills to provide value and you'll get the job.

In the age of the internet where everything is pretty much free, why do college degrees matter?

A college degree makes it easier to get the skills to get a job in machine learning or data science.

College degrees also help you make many useful connections and provides many opportunities in the form of internships and whatnot.

College degrees have their own place

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Pratham Prasoon
@PrasoonPratham

Are you planning to learn 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)

Pratham Prasoon
@PrasoonPratham

Machine learning terms you must know about as a beginner.

🧵👇

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)

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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Mitesh Patel
@Mitesh_Engr

Again sharing bank nifty intraday weekly straddle. How to play by selling straddle.
Mostly will work on Monday to Wednesday. Sometimes on Friday.
Why to pay your money to learn this if u can get it here.
U need only practice.
Let’s go.

Say bank nifty future is at 32000.
Sell both 32000 call and put.
Considering current volatile market lest say premium on Monday morning is 350 on both sides.
Sold with 700 points combine premium.
If bank nifty will trade in narrow range say +\- 100 hold it.
If moving 200 points

then exit previous sold straddle. Sell new one. Let’s say BN moved +200
Now sell 32200. If bank nifty will move here within range then u will get profit through theta decay. Follow same procedure for next 150-200 pts move.

This is only for single straddle sell position.

Now in second case sell three straddle. 31900/32000/32100
In case of 100 points move no need to change.
If 200 + then future is 32200.
Exit 31900 and sell 31200 straddle.
If moving another 100 then exit 32000 and sell 32300. Continue same with 100 points move.
If no move then

it will consolidate between 32000-32200 or 32100 to 32300 and u will get theta decay.

This is for normal move. Sudden big move may come anytime. So keep SL in system.
If u r selling at 350-350 then keep SL in system 475 for both legs.
Practice it. No need to attend webinar.

$Anumah, Abdulraheem Okehi \U0001f468\u200d\U0001f52c\U0001f1f3\U0001f1ec \U0001f1eb\U0001f1f7 \U0001f1f5\U0001f1f9 \U0001f1ea\U0001f1f8$

Anumah, Abdulraheem Ok...
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Department List of UCAS-China PROFESSORs for ANSO, CSC and UCAS (fully or partial) Scholarship Acceptance
1) UCAS School of physical sciences Professor
https://t.co/9X8OheIvRw
2) UCAS School of mathematical sciences Professor

3) UCAS School of nuclear sciences and technology
https://t.co/nQH8JnewcJ
4) UCAS School of astronomy and space sciences
https://t.co/7Ikc6CuKHZ
5) UCAS School of engineering

6) Geotechnical Engineering Teaching and Research Office
https://t.co/jBCJW7UKlQ
7) Multi-scale Mechanics Teaching and Research Section
https://t.co/eqfQnX1LEQ
😎 Microgravity Science Teaching and Research

9) High temperature gas dynamics teaching and research section
https://t.co/tVIdKgTPl3
10) Department of Biomechanics and Medical Engineering
https://t.co/ubW4xhZY2R
11) Ocean Engineering Teaching and Research

12) Department of Dynamics and Advanced Manufacturing
https://t.co/42BKXEugGv
13) Refrigeration and Cryogenic Engineering Teaching and Research Office
https://t.co/pZdUXFTvw3
14) Power Machinery and Engineering Teaching and Research

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Jaya_Upadhyaya
@Jayalko1

SRISAILAM: KASHI OF THE SOUTH (AP)
#andhratemples

The divine abode of Mallikarjuna Swamy (Jyotirlinga) and Devi Brahmaramba (Shaktipeeth).

When Ganesha was married before Kartikeya, he left in anger to Kraunch mountain. Shiva and Parvati also followed him there.

Shiva assumed the form of Jyotirlinga and resided on the mountain by the name of Mallikarjuna. Mallika means Parvati while Arjuna is another name of Shiva
It is also said that once, a princess Chandravathi, saw a cow shedding milk on a rock resembling a shivlingam.
@DubeyYash1911

She started praying to the lingam daily and used to offer jasmine (mallika pushpam) daily. Pleased Shiva appeared before her and agreed to wear jasmine garland (mallika pushpmala) on his head permanently. Since then, Shiva became known as Mallikarjun Swamy.

At this place, Shakti took the form of Bhramaram (bee) to kill Arunasur and settled as Bhramaramba Devi. The place is a Shakti Peeth as the neck portion of Devi Sati fell here.

It is said that by merely seeing the hill, one is emancipated from all his sins and worries.

$MAKE THE WORLD GREAT AGAIN \U0001f30d$

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The take down of the Cabal A-Z Charlie Freak & @djmarkdevlin

...In May 2017- Prince alwaleed tala sold his soul to the 13 bloodline families

Thread.. please retweet this truth 🙏

Trump visits the Middle East and receives royal Saudi welcome, highest Saudi honour by receiving the golden collar of abdulaziz al Saud
Not been seen with past presidents ...

When trump arrived in the summer of 2017 14/09/2017 he laid out all the documents, all the folders and they had all the photographs, disks, cds, dvds with all footage of them doing horrific things, the NSA has it all!

2 weeks later the richest men begun getting arrested...

They hand picked Obama, alwaleed tala decided to make Obama the linchpin of the us presidency. Barack has connections all over the cabal

SK
@sk_diary1

Prasanna Venkatesa Perumal #temple -Gunaseelam,Near Trichy,TN #Thread

Gunaseela Mahatmyam,a chapter of Bhavishyottara Purana describes greatness of this place.

Venkatachlapathi of Tirumala Tirupati appeared b4 Gunaseela Rishi here,so Gunaseelam is known as Ten(South)Tirupati

Prasanna Venkatesa Perumal is in a standing posture with 4 arms holding sankha & chakra in the upper hands.The lower right hand is in abhaya pose,blessing devotees &left hand is in kati hasta,resting on waist.

A sceptre(senkkol)rests on right shoulder,with which this murti is believed to cure people affected by mental illness.These patient stay here for a period-48 days& participate in poojas&most of them get relieved of their problems.There is a temple run Mental Rehabilitation center

#storytimethread -As per Sthala Puranam,existence of this temple is due to efforts of Gunaseela Rishi, a disciple of Thalpiya Maharishi.
Thalpiya Maharishi along with his disciple Gunaseela Rishi once went to Himalayas.On his way back, Gunaseela Rishi stayed back at Tirupathi.

He was so wanted to have darshan of Venkatachalapathy in Gunaseelam near his asharam & bless the devotees.

He came back, bathed in the Cauvery,took blessing from his guru &undertook severe penance here in his ashram in Gunaseelam.