BC MACHINE LEARNING
Saved by @Mollyycolllinss

1/ It's probably not the first thing you think of, but when we started .NET (COM+) in the late 90s, C# didn't exist yet. We were working on it at the same time as the CLR and the framework. So, you might wonder, what language was being used to generate IL and write the BCL?

 2/ The answer is a language that we called SMC that Peter Kukol wrote the compiler for. Peter is a flat out amazing engineer and wrote the core parts of the compiler in just a few days. This unblocked the framework team, allowed vetting the runtime and interpreter, etc.
3/ SMC was a trimmed down C++ variant and the compiler was written in itself (i.e. SMC). It didn't support things like destructors, multiple inheritance, virtual base classes, etc. But, overall it enabled progress that would have otherwise been stalled.
4/ It did introduce a problem for the C# language and compiler team though. Every day more and more code was written in SMC, a language that we did not intend to ship externally. Designing a new language is hard (well, at least, a general purpose one that becomes popular).
5/ So there was a constant balancing act between laying enough of the foundation of the language and the C# compiler to replace SMC and making sure that the design lived up to the goals and ideals the team was striving for. If we waited too long to adopt C#,
6/ there would be too much SMC code written and we might not be able to reasonable port all of it. If we moved too early, the C# language wouldn't have time to bake and we would sacrifice the design aesthetic for expediency.
7/ Folks know how this ends - we eventually felt good enough about the state of the C# language and compiler to begin the migration and ultimately converted the entirety of the framework. The design team was incredibly disciplined and held a hard line
8/ on the design of the language. As far as I can remember there wasn't a single language concession that was made in order to 'rush', despite the pressure. That isn't to say there was no impact though. I'll talk about what it meant for the compiler and IDE in a separate thread,
9/ but there were a small number of features added to unblock the BCL and make migration easier. For example, the __ keywords, like __arglist, __makeref, etc. It also meant that the design team started the practice of meeting 3x a week for multiple hours a day to resolve design
10/ issues. Looking back, it was a heady time, stressful, but exciting.

More from Machine learning

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
How to learn Python for machine learning. 🐍

The beginner's guide.
(based on my real life experience of mentoring 300+ students)

🧵👇

In this thread we'll look at all the concepts in Python you need to know for machine learning along with all the free resources that you'll need.

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 , PYTHON USEFUL
Santiago
Santiago
@svpino
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.

(2 / 16)

Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

(3 / 16)

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.

(4 / 16)

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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MACHINE LEARNING , TENSOR BASICS
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Unknown \U0001f464
Unknown 👤
@console_404
10 PYTHON 🐍 libraries for machine learning.

Retweets are appreciated.
[ Thread ]


1. NumPy (Numerical Python)

- The most powerful feature of NumPy is the n-dimensional array.

- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.

Ref: https://t.co/XY13ILXwSN


2. SciPy (Scientific Python)

- SciPy is built on NumPy.

- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.

Ref: https://t.co/ALTFqM2VUo


3. Matplotlib

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

- You can also use Latex commands to add math to your plot.

- Matplotlib makes hard things possible.

Ref: https://t.co/zodOo2WzGx


4. Pandas

- Pandas is for structured data operations and manipulations.

- It is extensively used for data munging and preparation.

- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.

Ref: https://t.co/IFzikVHht4
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
Santiago
Santiago
@svpino
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?

👇
MACHINE LEARNING

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Anu Satheesh \U0001f1ee\U0001f1f3
Anu Satheesh 🇮🇳...
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Kitta was from a low caste family. He was a great devotee of Guruvayurappan. Everyday before going to work he always prayed infront of a picture of Guruvayurappan in his house. His only wish was to worship at Guruvayur & have annadanam in temple. But he was not allowed there


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Shri Krishna, dressed himself as a Brahmin went to temple Kitchen. He packed food and went directly to kitta's house. Reaching Kitta's house, Shri Krishna told he was sent by a cook of Guruvayur temple and gave them the food he packed. Kitta and family was very happy seeing food.


When Kitta finished his food, Brahmin asked it will be better if he get a tender coconut( karikku)to drink. Kitta climbed the tree and gave all coconuts to him. After having 1 tender coconut, the Brahmin asked to bring remaining coconuts to Guruvayur temple the next day.
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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
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5. Copy/paste again on Medium.
6. Again on LinkedIn
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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.


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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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Melissa (she/her)
Melissa (she/her)
@LiteraryMouse
In regards to the #CapitolCoup, I’m in a unique position. I monitored forums ahead of time and then was present for the coup itself.

These are my observations based on what I saw and the evidence that’s been collected so far. I reserve the right to adjust my analysis as needed.

Let’s talk about the organization of the coup first. Seeing some people insist that the coup must have been highly planned and coordinated.

Yes and no. This was a #StochasticCoup. We’ll see little evidence of direct communication between Trump and the insurrectionists.

Within the insurrectionists, there was not one group that executed the storming of the Capitol.

Rather, there were a number of smaller groups working independently towards the same goal. I expect you’ll see little evidence that these various groups communicated with each other.

What you will see is detailed planning and coordination within these small groups.

Then there were the joyriders, those Trump supporters who weren’t directly involved in any plotting but who were more than happy to go along for the ride once the coup was in motion.

After four years, the insurrectionists were carefully attuned to Trump’s messaging.

Every tweet and statement of his was treated as marching orders and carefully dissected for meaning.

It’s why shutting down Trump’s Twitter account was so important.
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Patrick McKenzie
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Most software founders, particularly in B2B, need to get radically better at sales. @Steli , who is the best person for explaining the scrappy stages in the beginning and then building out a team with scripts and processes, wrote a guide to:

For technical founders it is irrationally, obscenely hard to reverse years of programming (ba dum bum) that sales is a value-destroying activity. Sales is CLEARLY a value-creating activity, contingent on you have a value-creating product.

The world will not drop what they are doing to adopt your work. This is particularly true in B2B, where simply building a better mousetrap won't overcome the activation energy required to get people with additional non-mice problems to prioritize changing mousetraps today.

This is very non-obvious for founders because founders are not often people who *want* to be sold to. We often come from a background where trying out tools is a bit of a fun hobby. We like looking at all the options, making charts, and ripping out partially complete tests.

"This week I unsuccessfully trialed four software options for automating that thing that has been killing us. Our actual production process remains the same as last week. Don't worry; this was a great use of time." is not a thing you want to write in a progress report to manager.
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Jonathan Ware
Jonathan Ware
@ReassessHistory
Rhino Barges

One of the dullest, coolest, more bizarre and fascinating pieces of kit used in Normandy.

Which NO ONE REALLY CARES ABOUT.

BUT I DO AND YOU SHOULD TOO.
/1

#WW2 #SWW #History


Planning for Overlord and Neptune had a serious snag, how to get troops from LSTs onto the beach as simply ramming them onto the beaches and dropping the ramp was known to damage the exceptionally vulnerable LSTs and felt to be unsustainable in the mid to long term. /2


LSTs were essential in sustaining Overlord's progress and were a subject of major headaches in the planning phase, and a real subject of friction when it came to launching additional amphibious operations such as Dragoon.

A single LSTs loss represented a capability nick. /3


So, logic dictated, we don't really want these remarkable but rare and highly valuable vessels constantly doing beach landings, we want something to act as a medium between ship to shore, and in came the (mostly) American designed Rhino. /4


I fucking love Rhinos.

Seriously.

Graceful lines of pure industrial pedigree.

Modular.

LOOK AT IT. /5
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