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
I sat next to one of the smartest, most successful software engineers in the country for over 10 years.

Here's what I learned.

Fast is 10 times better than good.

9 out of 10 times, a "good enough" solution is all you need to unlock time, money, and attention.

Focus relentlessly on being first. Then take the time to be right.

Fast, then right.

Ship more frequently.

The faster you show your work to your users, the faster you'll hear their feedback, you'll adapt, and you'll ship again.

A question you should ask every day:

What can I do to ship this one day before planned?

Technical debt is a good thing.

Everyone hates technical debt. I think they don't know how to take advantage of it.

When used appropriately, technical debt means working on what truly matters and deferring anything that can wait.

No debt ⟶ You waited too long.

Ask, don't think.

People burn their time every day instead of asking for help.

You don't get extra points for working harder, especially when a little help will make you faster.

Time is your most valuable resource. Don't waste it.
DS
Santiago
Santiago
@svpino
11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

🧵👇

1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.

2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.

3. Write a function that finds the duplicate number in an unsorted array containing every number from 1 to 100.

Only one of the numbers will appear twice.

4. Write a function that removes every duplicate value in an array.

There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.
TECH
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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
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.
TECH
Santiago
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.
ALL

More from Machine learning

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Everything you need to know about the math for machine learning as a beginner.

🧵👇

Before diving into the math, I suggest first having solid programming skills.

For example👇

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In Python, these are the concepts which you must know:

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

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If you want to learn python, these courses are freecodecamp could be of help to you.

🔗Basics: youtube .com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube .com/watch?v=HGOBQPFzWKo

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You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

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MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
These are the tools you will need for machine learning in Python.

🧵👇

Anaconda

When you work in python, you'll be working with several frameworks and many of them work only on specific versions of python.

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Now imagine downloading a new version of python and then installing it for every framework you want to work with 😬.

Meet Anaconda which allows you to run several versions of python. It comes pre-installed with several data science and machine learning frameworks.

(3 / 13)

Pip-env is also a way of maintaining several versions of Python and comes pre installed with Python.
You can use pip env or Anaconda, whichever works for you.

(4 / 13)

Jupyter Notebooks

Jupyter notebooks is an IDE just like VS code or Sublime. The special thing about jupyter is that you can parts of code in mini code editors called cells. This is great for prototyping and testing code.

(5 / 13)
MACHINE LEARNING
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Jan Giacomelli
Jan Giacomelli
@jangiacomelli
🤔 Python generators

What are they? How to use them?

#Python

🧵Let's find out 👇

1️⃣ Python generators are lazy iterators delivering the next value when their .next() is called.

They are created by using the yield keyword

next() can be called explicitly or implicitly inside for loop

They can be finite or infinite


2️⃣ yield - where a value is sent back to the caller, but the function doesn’t exit afterward as with the return statement

The state of function is remembered.

For example, the number is incremented and sent back from yield at the consecutive call next()


3️⃣ Generator stores only the current state of the function - it generates next element on next() call and forgets the previous one -> it saves memory

For example, we don't need to store 1mio elements in memory to do something with each element


4️⃣ When you call a generator function generator object is returned - it's not executed yet

It executes only when next() is called

For example, that's why Exception is raised only on the next() call
MACHINE LEARNING
Prashant \U0001f4da
Prashant 📚
@capeandcode
Ever heard of Autoencoders?

The first time I saw a Neural Network with more output neurons than in the hidden layers, I couldn't figure how it would work?!

#DeepLearning #MachineLearning
Here's a little something about them: 🧵👇


Autoencoders are unsupervised neural networks whose architecture you can picture as two funnels connect from the narrow ends.

These networks are primary focus for compression tasks of data in Machine Learning.

We feed them the data so that they can learn the most important features, a smaller representation while keep the integrity of the data.

Later when someone needs, can just take that small representation and recreate the original, just like a zip file.📥

Being unsupervised, they require no labels.
Our inputs and outputs are same and a simple euclidean distance can be used as a loss function for measuring the reconstruction.

Of course, we wouldn't expect a perfect reconstruction.

We can think of an autoencoder having two components, encoder and decoder, represented by the below equations:

We are just trying to minimize the L here. All the backpropagation rules still hold.
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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Thirumalai Murugan Kovil
#Panpoli
#TamilNaduTemples 🚩

Thirumalai Murugan Temple is located in Tenkasi district of Tamil Nadu. Muruga is worshipped as Thirumalai kumarasamy. Temple is situated near Kerala border and known to be where Shri Murugan gave darshan to Rishi Agasthiar


Devi is worshipped in the temple as Thirumalai Kaali amman

Sthala Puranam says that, there was a priest named Poovanbhattar of Thirumalai Kaali amman. Murugar appeared in a dream of the bhattar and said to find a Vigraham, lying in particular place which is of Thirumalaikumaran


Bhattar conveyed the news to the Pandalam Raja and they went to find out the vigraham. Surprisingly ants showed the way to find the Vigraham. Thus Murugan vigraham was found and Prathista was done here. There is a lake with pure water on the top of the hill


which is said to be created by Agasthiyar. It is believed that kannaki of
Chilapathikaram visited here before going to Kerala. Sapthamathrukkal also worshipped Murugan here. Kanthashashti Thiruvizha is very famous here.

Om Muruga
Muruganukku Harohara
🙏🕉🙏
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sriram
sriram
@SriramKannan77
Sri Alarmelmangai Thayar sametha Sri Prasanna Venkatesa Perumal, at Thirumalai Vaiyavur, Chengalpat, near Chennai.

ancient hill temple of (more than 500 years old), Lord Prasanna Venkateswara in the sanctum sanctorum looks majestically holding the scepter-Sengole.


Two Thayar idols, Mahalakshmi and Bhu Devi, are on His broad chest on both sides.

Divine serpent Adi Sesha has his place in the Tiruvasi frame around the Lord in upside down 'U' letter shape with all craftsmanship.

Lord is wearing 'Ashalakshmi-Sahasranama' malas and 'Dasavatara Odyanam', the belt around the waist.

Tiruvonam star days in the months of Aavani (August-September) and Purattasi (September-October) are celebrated grandly in the temple.

In Purattasi Tiruvonam, rice nivedhanam is arranged in the shape of Seven Hills with seven dheeps, seven varieties of other preparations and vegetables.

Om Namo Venkatesaya!
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Karol296
Karol296
@Karol296
Wracając do sedna dzisiejszego zamieszania z artykułem autosportu / motorsportu. Kubica mówi w nim, że podejmie swoją decyzję w ciągu kilku dni. Rozważa opcję z Williamsem lub posadę kierowcy symulatora w Ferrari. Więc za kilka dni dowiemy się co go czeka za rok. #F1PL

Autosport / motorsport zmienił nagłówek swoje tekstu, bo pierwotnie sugerował on że Kubica jest numerem jeden na liście Williamsa na tytuł mówiący tylko tym, że nadal jest na liście kandydatów. Zrobili to po interwencji zespołu. Same wypowiedzi Kubicy z wywiadu się nie zmieniły.

Trudno powiedzieć czy autor się pośpieszył i wyciągnął inne wnioski niż powinien, a może wie coś więcej, ale nie mógł tego otwarcie napisać. Słowa Kubicy brzmią jednak jednoznacznie: decyzję podejmie w ciągu kilku dni.
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WORLD OF SANATAN DHARMA
WORLD OF SANATAN DHARM...
@world_sanatan
WHY BABUR NEVER TRIED TO ATTACK SRI KRISHNADEVARAYA ?

A🧵u must read

It is very well known that Babur considered Krishna Dev Raya as the strongest ruler of the entire subcontinent, and Vijayanagar empire the strongest during that time in India.

1/10


The Vijayanagar empire reached its peak during 1509-1529 around the reign of Krishna Dev Raya.

Vijayanagar’s famed elephant brigade 👇

2/10


At his command there were over 50000 elite troops with a regiment of Portuguese gunners and 3200 cavalry with 600 elephants.

3/10

Along with that more than four hundred thousand ( four lakh) peasant levies and irregular military made this empire one of the largest in all of South Asia.

A typical Vijayanagar levie soldier 👇

4/10


During Baburs invasion, Krishna Dev Raya ruled supreme in all of deccan.

In fact, in an all our brawl, Krisha Dev Raya will beat Babur fair and square.

Babur could muster at max 50000 troops with 50 canons.

While Krishna Dev Raya had these numbers in Hampi alone

5/10
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