BC MACHINE LEARNING

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Santiago
@svpino 4 years, 4 months ago 1127 views

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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.

👇

5⃣ Focus on the analysis and not the code

In the beginning, spend your time and energy analyzing the problem and its solution.

Code is not important at this stage. Code can come later.

👇

6⃣ Start incorporating new algorithms

As you work through problems, start incorporating new algorithms into your toolset.

Here are a few great options to start:

1. Decision Trees
2. Linear regression
3. Logistic regression
4. Neural Networks
5. KNN

👇

7⃣ Get familiar with a general process to approach problems

Here is a good start:

1. Define the problem
2. Prepare the data
3. Spot-ccheck algorithms
4. Improve the results
5. Present the results

👇

8⃣ Pick a new problem and repeat

It shouldn't be surprising that the best way to improve is to practice and solve new problems.

If you don't have access to real-life problems, get familiar with Kaggle: everything you need will be there.

In the next coming weeks, I'll be posting a whole series of machine learning advice for people wanting to start.

Stay tuned!

More from Santiago

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

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.

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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

My recommendation to start learning how to deal with structured data: Kaggle's Pandas Tutorial.

• Creating, Reading, Writing
• Indexing, Selecting, Assigning
• Summary Functions, Maps
• Grouping, Sorting
• Data Types, Missing Values
• Renaming, Combining

100% Python 🐍.

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.

↓

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 → 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 → Potential Solution → Data.

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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:

Jan Giacomelli
@jangiacomelli

🤔 Python decorators

What are they? How do you use them?

🧵 Let's find out 👇

1️⃣ Decorator is a function or a class that wraps another function or class modifying its behavior.

So how does that work?

The first thing to know is that everything in Python is an object - functions too

2️⃣ That means they can be passed to another function as an argument or returned from a function

Functions that take other functions as an argument are called higher-order functions

3️⃣ In Python, you can define a function inside other function - such functions are called inner functions

4️⃣ To create a decorator you just need to apply all of that together

log_enter_leave is a decorator.

my_function is the function.

To alter my_function's behavior we reassign it applying log_enter_leave decorator.

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Roger Grosse
@RogerGrosse

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

$Pratham Prasoon \U0001f680$

Pratham Prasoon 🚀...
@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)
🧵👇

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 here👇
🔗//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 YouTube👇

Machine Learning foundations course
🔗//youtu.be/_Z9TRANg4c0

Code👇
🔗//colab.research.google.com/github/PrasoonPratham/Kaggle/blob/main/MNIST.ipynb

(5 / 22)

Pratham Prasoon
@PrasoonPratham

Here's what you'll hear when you first get into machine learning.

- Neural networks
- Loss
- Weights
- Biases
- Epochs
- Neurons
- Optimizers
...

It can get very confusing really fast!

Here are some of the terms you should know about.
(I wish I had this before)

🧵👇

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.

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.

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.

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.

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Anu Satheesh
@AnuSatheesh5

Puthucode Annapoorneshwari Temple,
Palakkad, Kerala 🚩
#keralatemples
अन्नपूर्णे सदापूर्णे शङ्करप्राणवल्लभे
ज्ञानवैराग्यसिद्ध्यर्थं भिक्षां देहि च पार्वति

Temple is also called Puthukode Bhaghavathy Kshetram
Parasurama did Prathishta of Devi here. Temple is one amoung 👇

108 Durga temples
Navarathri festival is celebrated in a grant manner here. Annadanam is done daily on these days. The Idichu pizhinja payasam/ Chathachatayam is the main nivedyam in Annapoorneswari temple during Navarathri.
In the mornings, Devi Mahatmyam and Sundarakandam are

recited here daily.
Sri Godavarma Raja gifted lots of rice fields for the maintenance of the temple.
Annapoorneshwari holds Shanku and Chakra in her hands. Puthucode Annapoorneswari Temple is managed by the Naduvil Madom Devaswom.
🙏🕉🙏

$ArmaniTalks \U0001f399\U0001f525$

ArmaniTalks 🎙🔥...
@ArmaniTalks

A relationship begins to get stiff when excessive patterns begin to form.

Too many patterns leads to a boring, predictable, logical lifestyle.

Get the emotions involved.

Do something spontaneous out of the blue moon & flip the script.

'What's wrong with the patterns?'

Nothing is wrong with patterns.

However, there is a problem when all you have are patterns.

It's boring.

'Can you explain why?'

Sure 👇

The thing with relationships is that it's meant to be an emotional experience.

It's very difficult to logically explain love, right?

'Right.'

Well this is where you want to steer the ship the right way.

1. Too emotional is bad.

2. Too logical is bad.

Let me break it down.

1. Too emotional

If a relationship is completely ruled by emotions, then it's too volatile.

You will never reach productivity if you are aimlessly only following each others emotions.

This lifestyle tarnishes any structure for the couple.

'So why do people do it?'

Because it's fun.

You may ruled by this stage in the honeymoon phase.

Normal.

You'll be so spontaneous that it almost becomes a lifestyle.

But it's hard to level up your life when you're too spontaneous.

Avoid.

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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

$Ranvijay Singh\U0001f1ee\U0001f1f3$

Ranvijay Singh🇮🇳...
@ranvijayT90

#Thread The legend of Ekadash Mukhi Hanuman ji...

Mahadev himself narrated the story of Ekadash Mukhi Hanuman ji to Maa Parvati. Once a great monster named Kalkarmukh performed the severe penance of Brahma ji.

Pleased with the austerity of the demon, Brahma ji gave him a boon that the person who will have eleven Heads like you and who was born on the day of your birth, only he/she will be able to kill you. No one else will kill you.

After getting a boon from Brahma ji, Kalkarmukh created a ruckus in all three lok. He invaded Swarg and snatched all the rights of the Devtas and became himself the king of Swarg.

All Devtagan came to Shri Rama and asked him for help. Hearing the agony of the Devtagan, Shri Ram called his supreme devotee Hanuman ji and said, "Pawan Putra, Sankatmochan".

$Coffee Sloth \u2615$

Coffee Sloth ☕
@CaseyExplosion

*Heads-up that this thread will feature some extreme examples of homophobia and transphobia.*

Hey folks, have you ever wondered why trans people face constant accusations of fetishism, sexual predation and child abuse/grooming? Well, today let's talk about stochastic terrorism.

So, first we have to talk about what stochastic terrorism actually is. Remember when there was a conspiracy that there was a pedophile ring operating out of a pizza parlour basement, and a crazed gunman showed up? It's that sort of incitement that stochastic terrorism describes.

It's the demonisation or incitement against a group of people or individuals with the intent that other, unaffiliated parties will act upon it; it's the releasing doctored footage and a shooter show up at Planned Parenthood as a direct result of what he's been led to believe.

So with that in mind, let's talk about Russia's extreme anti-LGBT laws that banned the "promotion" of LGBT lifestyles to children. A move that specifically marked LGBT people as a threat to children, and resulted in neo-nazis meting out vigilante "justice" against gay men.

Groups such as this would lure unsuspecting gay men via dating sites, and brutalise and humiliate them on camera. They did so under the guise of combating child abusers, one group calling themselves "Occupy