BC MACHINE LEARNING
Saved by @Alex1Powell

An introduction to one of the the most basic structures used in machine learning: a tensor.

🧵👇

 Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

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Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

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The rank of a tensor refers to the tensor's number of axes.

Examples:

▫️ The rank of a matrix is 2 because it has two axes.
▫️ The rank of a vector is 1 because it has a single axis.

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The shape of a tensor describes the number of dimensions along each axis.

Example:

▫️ A square matrix may have (3, 3) dimensions.
▫️ A tensor of rank 3 may have (2, 5, 7) dimensions.

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The data type of a tensor refers to the type of data contained in it.

For example, when thinking about Python 🐍's numpy library, here are some of the supported data types:

▫️ float32
▫️ float64
▫️ uint8
▫️ int32
▫️ int64

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In the previous tweets I used the terms "vector" and "matrix," to referr to tensors with a specific rank (1 and 2 respectively.)

We can also use these mathematical concepts when describing tensors.

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A scalar —or a 0D tensor— has rank 0 and contains a single number. These are also called "0-dimensional tensors."

The attached image shows how to construct a 0D tensor using numpy. Notice its shape and its rank (.ndim attribute.)

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A vector —or a 1D tensor— has rank 1 and represents an array of numbers.

The attached image shows a vector with shape (4, ). Notice how its rank (.ndim attribute) is 1.

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A matrix —or a 2D tensor— has rank 2 and represents an array of vectors. The two axes of a matrix are usually referred to as "rows" and "columns."

The attached image shows a matrix with shape (3, 4).

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You can obtain higher-dimensional tensors (3D, 4D, etc.) by packing lower-dimensional tensors in an array.

For example, packing a 2D tensor in an array gives us a 3D tensor. Packing this one in another array gives us a 4D tensor, and so on.

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Here are some common tensor representations:

▫️ Vectors: 1D - (features)
▫️ Sequences: 2D - (timesteps, features)
▫️ Images: 3D - (height, width, channels)
▫️ Videos: 4D - (frames, height, width, channels)

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Commonly, machine learning algorithms deal with a subset of data at a time (called "batches.")

When using a batch of data, the tensor's first axis is reserved for the size of the batch (number of samples.)

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For example, if your handling 2D tensors (matrices), a batch of them will have a total of 3 dimensions:

▫️ (samples, rows, columns)

Notice how the first axis is the number of matrices that we have in our batch.

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Following the same logic, a batch of images can be represented as a 4D tensor:

▫️ (samples, height, width, channels)

And a batch of videos as a 5D tensor:

▫️ (samples, frames, height, width, channels)

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If all of this makes sense, you are on your way! If something doesn't click, reply with your question, and I'll try to answer.

Either way, make sure to follow me for more machine learning content! 2021 is going to be great!

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More from Santiago

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.
ALL , DS , ADS
Santiago
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?

👇
ALL , MACHINE LEARNING
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Santiago
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."

👇
MACHINE LEARNING
Santiago
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.
DS
Santiago
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 🐍.
ALL

More from Machine learning

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Are you planning to learning machine learning this year?

These are the Python frameworks you need to learn as an absolute beginner.

🧵👇

It can be overwhelming seeing all the machine learning frameworks at once and not even knowing what they do.

I made this thread so that your machine learning journey becomes just a little bit easier.

(2 / 11)

This thread will only focus on Python frameworks.

Why?
Because Python is the most common path into machine learning.

( If you have experience with any other language then learning Python will not be difficult at all. )

(3 / 11)

Here's the entire list:

- Pandas
- Numpy
- Matplotlib
- TensorFlow
- PyTorch
- Keras
- Scikit Learn

Let's see what they do.

(4 / 11)

You'll have to interact with csv files or databases on a regular basis to access data in machine learning, pandas helps you do just that.

The cool thing about Pandas is that it actually doesn't change the original data...

(5 / 11
MACHINE LEARNING
Madison Kanna
Madison Kanna
@Madisonkanna
With hard work and determination, anyone can learn to code.

Here’s a list of my favorites resources if you’re learning to code in 2021.

👇

1. freeCodeCamp.

I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things

2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:

1) You can see the most upvoted solutions so you can read really good code

2) You can ask questions in the discussion section if you're stuck, and people often answer. Free

3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE

On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.

4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.

Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
MACHINE LEARNING
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Santiago
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."

👇
MACHINE LEARNING
Pratham Prasoon
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.
MACHINE LEARNING
Caio Rolla
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.
MACHINE LEARNING

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Enigma
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@ainittomai
On December 25 Mr Pool retweeted August 16 message about the Phoenix and that the worst was still on the horizon to come. 1/*


Maybe now we can assume that November 3 message meant "FUD" nor "FUN". Putting the word in capital letters seems to indicate the aspect the word fud has in our space, FUD. 2/*


Brad started it on December 22 (1/3 posts (Don't say? Sure) = 13 = Plan B). 3/*


And immediately the SEC followed dropping the bomb (1.3 billions (Don't say? Sure) = 13 = Plan B). 4/*


FUD materializes, Bitstamp is one of the first that haltes XRP trading and so on. 5/*
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Joey Tawadrous 🇮🇪...
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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
- talk/sell to customers to stress test your idea

Nothing else.
STARTUPS
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Nikita Poojary
Nikita Poojary
@niki_poojary
Catch me if you can @Mitesh_Engr

Time for a🧵
Mitesh Sir's EXPIRY Option Selling 101:
• What to look for?
• Strike Selection & Ratios
• SL mgmt
• Avoiding freezes
• Monthy Expiry
• Event days
• How he would have traded last expiry?

In collaboration with @AdityaTodmal


Edward de Bono says intelligence is like a🚗 Automobile

“A powerful car may be driven badly. A less powerful car may be driven well. The skill of the car driver determines how the power of the car is used.”


What @Mitesh_Engr Sir looks at for Expiry Trading:

• Technicals of BNF
• Individual Heavyweight stocks of BNF
• Support/Resistance of BNF on weekly TF
• Also has Superb SL management
• When it comes to adjustments, he is always on toes


How to Select Strikes to Sell?

• You can't sell based on premium
• Follow only trend
• Price action is best to sell on expiry day
• Never wait for the options to go to zero


Using Ratios to Sell:

• If BNF is in a range then strangles
• If breakout/ breakdown from a range then directional
• Selling 70/30 or 80/20 ratios
• 2 examples on how he used ratios
ALL , MITESH PATEL OPTIONS STRAT , MITESH PATEL EXPIRY GUIDE
Dr. Jane Clare Jones
Dr. Jane Clare Jones
@janeclarejones
So, on the subject of bonkers hyperbolic pretzeling over the Bell judgement, Grace 'destroy books I don't like & make inappropriate jokes about sterilising teenage girls' Lavery has some thoughts.

Tell me why my feminism is wrong Grace.

Oh

Well, if anyone thought the Bell judgment was going to make TRAs reconsider making massively overblown claims with no evidence backed up with nothing but a thick wadge of emotional blackmail.... HAHAHA, no one thought that.


A high court in the UK made a delimited judgment about teenager's ability to consent to puberty blockers. This puts all trans people everywhere in the world at risk.

Because if any human anywhere has any thoughts that deviate in any way from the rote line dictated by

the trans rights movement, this puts all trans people everywhere in mortal danger.

Let's be honest Grace. It doesn't put trans people at risk. It puts trans ideology at risk. Because trans ideology depends on the idea of innate gender identity, and the trans child is the

necessary material evidence of the ontology of gender identity.

That is, children are being medicalised to provide evidence to underwrite adults identities.

Nothing to see here.
LAW
Egypedia
Egypedia
@Egypediax
Ancient Egypt was the most advanced civilization in the ancient world. Why was Egypt so special?

There are so many firsts in Egypt that we all know about, and that makes Egypt unique. For example, the Sumerians did not build the pyramids of Egypt.


Save as history
HISTORY