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
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.
ALL
Santiago
Santiago
@svpino
Many machine learning courses that target developers want you to start with algebra, calculus, probabilities, ML theory, and only then—if you haven't quit already—you may see some code.

I want you to know there's another way.

↓

2. For me, there's no substitute to seeing things working, trying them out myself, hitting a wall, fixing them, seeing the results.

A hands-on approach engages me in a way pages of theory never will.

And I know many of you reading this are wired just like me.

3. I feel that driving a car is a good analogy.

While understanding some basics are necessary to start driving, you don't need to read the entire manual before jumping behind the wheel.

As long as you practice in empty parking lots and backroads, you'll be fine.

4. As you make your way to public roads, you can start incorporating more of the theory that will help you stay safe.

At this point, that theory won't be lost on you: your hours behind the wheel will help you make the necessary connections.

Things will start clicking quick.

5. I've talked to people struggling with derivatives that have no idea why or when they'll become helpful.

I've seen others memorizing what eigenvectors are, or manually transposing matrices because "that's what it takes."

Honestly, for the most part, it's not.
READ IT
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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
Santiago
Santiago
@svpino
The two main machine learning techniques used in the industry today:

1. Gradient Boosted Trees
2. Deep Learning

Focus your time learning Scikit-Learn, XGBoost, and a Deep Learning library like Keras or PyTorch and you'll get the most for your time.

If you need to deal with structured data: Scikit-Learn + XGBoost.

If you need to deal with unstructured data (perceptual tasks): Keras or PyTorch.

Of course, there are many more techniques that will be helpful, but if you focus on these you'll be maximizing your ability to deliver value in the short term.
DS
Santiago
Santiago
@svpino
A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

↓ 1/5

"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression
ALL

More from Machine learning

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
There are several skills needed for learning machine learning that no one talks about.

Here are some of them.
(from what I've learned over the past two years)
🧵👇

This thread aims to introduce to you some of the skills often ignored for learning machine learning but are actually very important.

These skills will enable you to learn concepts quickly and more efficiently in this field.

(2 / 9)

1⃣ Reading

Probably one of the most underrated skills on this list.

In machine learning, you HAVE to read a lot of articles, papers, documentation, and whatnot.

It is mostly due to the theoretical nature of this field that reading is an important skill to have.

(3 / 9)

2⃣ Strong fundamentals in programming

Machine learning is just a lot of programming mixed with math and data.

Having clear programming fundamentals is crucial for this field.

I highly suggest you learn about these if you are using Python for machine learning.👇

(4 / 9)

- Object oriented programming in Python :Classes, Objects, Methods
- Lists & List functions
- Dunder Methods
- List comprehension
- List slicing
- String formatting
- List, Dictionaries & Tuples
- *args,**kwargs

(5 / 9)
MACHINE LEARNING
Pratham Prasoon
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.

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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)
MACHINE LEARNING
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Pratham Prasoon
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)
MACHINE LEARNING , ALL , PYTHON STUDY
Pratham Prasoon
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)
MACHINE LEARNING , PYTHONML
emmi
emmi
@emmibevensee
The "polarization is the problem" narrative to me is bad because it implies the status quo was a stable and acceptable set of trade-offs. It's the equivalent of a pollyanna "why can't we all get along" but the we in question is white supremacy and its targets.

like i understand that conflict mediation requires mutual concessions (i did my MA in conflict studies) and while it's important to meet people's deep needs across spectrums of difference, certain things are just not up for negotiation.

Also while peacebuilding work is good. It's not just "seeing each other as human" it's literally about transforming structural issues. (hence Galtung's distinction between positive and negative peace wherin negative peace is like a ceasefire and positive is a transformed society.

And I think that to whatever extent that kind of peacebuilding work is applicable, it's not between fascists and people who think fascists are bad. Fascists are not invited. They are only invited if they seize territories and can't be crushed. Then secret negotiations.

The US is always like "we don't negotiate with terrorists" but literally every president negotiates with who they consider to be terrorists. It's all just down low. But that's just because counter-insurgency failed.
MACHINE LEARNING

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The benign product is sovereign programmable money, which is historically a niche interest of folks with a relatively clustered set of beliefs about the state, the literary merit of Snow Crash, and the utility of gold to the modern economy.

This product has narrow appeal and, accordingly, is worth about as much as everything else on a 486 sitting in someone's basement is worth.

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And here they are...

THE WINNERS OF THE 24 HOUR STARTUP CHALLENGE

Remember, this money is just fun. If you launched a product (or even attempted a launch) - you did something worth MUCH more than $1,000.

#24hrstartup

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https://t.co/M75RAirZzs

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Really impressive for 24 hours. Congrats!


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SayHenlo - Chat without distractions

https://t.co/og0B7gmkW6

Built by @DaltonEdwards, it's a platform for combatting conversation overload. This product was also coded exclusively from an iPad 😲

Dalton is a beast. I'm so excited he placed in the top 10.


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CoderStory - Learn to code from developers across the globe!

https://t.co/86Ay6nF4AY

Built by @jesswallaceuk, the project is focused on highlighting the experience of developers and people learning to code.

I wish this existed when I learned to code! Congrats on $250!!
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Vibhu Vashisth
Vibhu Vashisth
@VIBHU_Tweet
Did you know?

When Saudi Arabia's King visited India in 1955, PM Nehru ordered all the Hindu Temples and Idols of Hindu Deities that came in Saudi King's way to be covered up.
Our temples are the architectural marvels in their own league.


Each of our temples tells the story of our rich history and cultural heritage. Was Nehru that ashamed of his own nation's heritage that he had to cover them up or there was some other reason? The answer would be best known to him only.

PM Nehru invited the then Saudi King Saud bin Abdul Aziz to India who accepted the invitation and planned a 17 day long official visit to India in 1955. Saudi King had brought a large delegation with him and he had to visit various Indian cities like Mumbai, Hyderabad, Agra,


Aligarh, Shimla, Mysore and Varanasi. However, when Saudi King reached Varanasi, Nehru ordered to cover all the temples and Idols with curtains that came in the way of the Royal Convoy. During his stay in Varanasi, Govt buildings were painted with the words "Kalma Tayyiba".


'Kalma Tayyiba' means 'There is not God but only Allah and Mohammed is his messenger'. In addition to it, Pooja and Aartis were also stopped at temples. I can call it the appeasement politics of the highest order and Nehru was indeed the greatest appeaser of all times.
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Nicolas Cole
Nicolas Cole
@Nicolascole77
🚢 Atomic Essays Republishing Framework 🚢

1. Write Atomic Essay
2. Post image on Twitter
3. (Bonus) Copy/paste text as thread
4. Find relevant Question on Quora. Copy/paste Atomic Essay + image.
5. Copy/paste again on Medium.
6. Again on LinkedIn
7. Win

Guide below ✍️🚢👇

Step 1: Write Atomic Essay

I really enjoy writing right inside the Figma template. It helps give me a good sense of exactly how much "real estate" I have/have left before my time us up and I'm out of space.

Once finished, I export the image and send to my phone on Slack.


Step 2: Post image on Twitter

Before I publish my Atomic Essay on Twitter, I use the Edit/Photo Markup function on the iPhone to highlight standout sentences.

These are usually power-phrases: things the reader skims and thinks, "That's interesting," prompting them to read more.


Step 3: For the Ship 30 for 30 challenge, I only turn Atomic Essays into threads if...

1. The piece is SUPER actionable and lends itself well to the thread format (lots of bullets, quick points, lists, etc.)

or...

2. I'm referencing research and want to link to the pieces.

Step 4: Find relevant Question on Quora. Copy/paste Atomic Essay + image.

Search on Quora around the topic you wrote about. Find a related Question. Copy/paste the whole Atomic Essay as your "answer" (& fix formatting). Then include image so it appears in the thumbnail.
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Jaya_Upadhyaya
Jaya_Upadhyaya
@Jayalko1
SRI CHAMPAKADHAMA SWAMY, BANNERGHATTA HILL, BENGALURU (KAR)
#bharatmandir

The shrine is dedicated to Sri Vishnu along with Devi Lakshmi and Bhoodevi.
It is built in an area full of Champaka trees.
As champaka flowers are offered to Sri Vishnu here, it is called Champakadhama.


It is believed that King Janmejaya had sarpa-dosha. To get rid of it, he took bath in the nearby pond. He emerged with a golden body and sarpa-dosha was also removed. The pond is called Swarnamukhi and believed to have curative powers.
Sri Vishnu murti was consecrated by him.


It is also believed that this forest area was the Dandakaranya where Sri Ram stayed in exile.
Some believe that the original shrine was built by Pandavas.
The pushkarni/pond is emptied once a year so that the rock at the bottom (which has image of Anjaneya) may be worshipped.
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