BC MACHINE LEARNING
Saved by @WebbDarrrel

Do you want to learn the maths for machine learning but don't know where to start?

This thread is for you.
🧵👇

 The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.

I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.

Read this thread for more details👇

https://t.co/sSN3jdxDwK
These are topics of math you'll have to focus on for machine learning👇

- Trigonometry & Algebra

These are the main pre-requisites for other topics on this list.

(There are other pre-requites but these are the most common)
- Linear Algebra

To manipulate and represent data.

- Calculus

To train and optimize your machine learning model, this is very important.
- Statistics

Make "sense" out of the data you have.

- Probability

Make decisions under uncertainty.
These are some of the resources I recommend for learning these topics 👇
Neural Networks

> A series of videos that go over how neural networks work with approach visual, must watch.

🔗youtu.​be/aircAruvnKk
Seeing Theory
> This website helps you learn statistics and probability in an intuitive way.

🔗seeing-theory.​brown.​edu/basic-probability/index.​html
Gilbert Strang's lectures on Linear Algebra (MIT)

> This is 15 years old but still 100% relevant today!
Despite the fact these lectures are made for freshman college students at MIT, I found it very easy to follow👌

🔗youtube.​com/playlist?list=PL49CF3715CB9EF31D
My Thread on Linear Algebra.

https://t.co/3H7U2HJgTd
The essence of Linear Algebra
> A beautiful playlist of videos which teach you linear algebra through visualisations in an easy to digest manner.

🔗youtube.​com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
Khan Academy
>You'll find a course on everything here! Khan Academy is the first place I'll go to when I want to learn something.

🔗khanacademy.​org/math
Essence of calculus
> A beautiful series on calculus, makes everything seem super simple.

🔗youtube.​com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x
The math for Machine learning e-book

> This book is for someone who knows quite a decent amount of high school math like trigonometry, calculus, I suggest reading this after having the fundamentals down on khan academy.

🔗mml-book.​github.​io

More from Pratham Prasoon

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
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
Advertisement
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Here's everything you need to know about the math for machine learning.

(+ free resources )
🧵👇

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

For example👇

(2 / 18)

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

(3 / 18)

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

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

(4 / 18)


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

It is 100% compulsory.

(5 / 18)
MACHINE LEARNING
Pratham
Pratham
@PrasoonPratham
A list of my favourite tutorials for learning Python as a beginner.

🧵 👇🏻

All the tutorials below include the basics like installation, variables etc.

I've also listed out the key highlights of each tutorial so that it is easy for you decide which one to pick.

Before going through these tutorials I would highly suggest you to go through this thread if you are a complete


The Full Python Course from learn with Python with Rune

key highlights
- 17 part course
- Jupyter notebooks
- Free eBook included

Duration: 8

The Python Beginner's course on FreeCodeCamp's YouTube Channel.

Key highlights

- Building a casic calculator
- Mad Libs Game
- Slightly advanced concepts like inheritance, Classes etc.

Duration: 4
ALL
Pratham
Pratham
@PrasoonPratham
This is a beginner's guide on mining cryptocurrencies.

🧵 👇🏻


Mining 101

Typically, when you transfer money using a service like Paypal, they take a small cut for facilitating the exchange.

In cryptocurrencies, people like you and me act as Paypal and facilitate exchanges of cryptocurrency. We get a cut for this just like Paypal did.

In order to make these transactions happen, our computers need to do some calculations which requires a lot of computational power.

A GPU or a Graphics Processing Unit which is typically marketed for gaming workloads can be used to mine cryptocurrencies.

Why do you need a GPU?

Today, there are so many miners that the "difficulty" of mining cryptocurrencies has skyrocketed, which basically means it takes a lot of computational power to mine crypto which GPUs can provide and CPUs cannot.

(❗ This is an oversimplification)

If you are interested in the inner workings of how blockchain and cryptocurrency, then I highly suggest that you read this thread by @oliverjumpertz
CRYPTOCURRENCIES GUIDE

More from 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
Roger Grosse
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.
MACHINE LEARNING
Advertisement
Kieran Snyder
Kieran Snyder
@KieranSnyder
1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.
MACHINE LEARNING
Cory Doctorow #BLM
Cory Doctorow #BLM
@doctorow
We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

1/


People who hold disfavored views struggle to find one another and mobilize. To find other people that feel the same way as you and make common cause with them to effect political change, you have to reveal your views and suffer social sanction.

2/

Search allows people who hold these views to find one another. If you have a deep feeling about your gender being nonbinary but don't know the words for it, you can search for communities of people who have those words, join them, and discover who you've been all along.

3/

This is why, in this moment, so many ideas are migrating from the fringe to the center: ideas about racial justice, gender identity, alternatives to market systems, etc. People have harbored these views all along, but have held back on expressing them.

4/

Being able to express yourself in private, among people who share your views, is a prelude to going public and putting your case to the wider world in hopes of effecting change.

5/
MACHINE LEARNING
Anson Horton
Anson Horton
@AnsonHorton
1/ @werat asked about whether the debugger was using the C# compiler or language service in VS 2002. It was not. The debugger has a component called an ‘expression evaluator’ that is provided per language and is responsible for parsing and evaluating expressions when stopped at a

2/ breakpoint. For example, if you type into the immediate window, hover over a variable, type into the watch window, etc. the expression evaluator is involved. The debugger and the language service are actually deeply integrated in a number of scenarios in VS, which may

3/ initially seem surprising. I may talk about more of these scenarios in the future, but to give a flavor, when you set a breakpoint at design time the language service is involved, when you are using Edit and Continue the LS is involved, the range of what is being evaluated

4/ when you get a debugger data tip, completion lists when typing in the data windows or immediate window, etc. Prior to VS 2002, there were distinct debug environments tailored to each target customer (VB/VBA, C++, VJ, VBScript, TSQL, FoxPro, Fortran, etc.). The goals of the

5/ VS 2002 debugger included unifying the debugger backend components and providing a single UI stack that would enable new scenarios (for example, a callstack across languages). Expression evaluators are the architecture that separate the language specific pieces from the
INTERNET , MACHINE LEARNING

You May Also Like

Vibhu Vashisth
Vibhu Vashisth
@VIBHU_Tweet
IT WAS RISHI KANADA WHO GAVE THE ATOMIC THEORY & TALKED ABOUT THE GRAVITY OF EARTH, 2600 YEARS BEFORE AN APPLE FELL IN FRONT OF NEWTON'S EYES:

Scientists of the modern world credit an English scientist John Dalton (1766 CE -1844 CE) for the Atomic Theory.


And this is because, most of us are not even aware of an ancient Indian Sage named Kanada, who was the first to formulate the theory of atoms, almost 2600 yrs before Dalton and talk about earth's gravity.


Rishi Kanada was born around 600 BCE in Dwarka, Gujarat and he was the son of a philosopher named Ulka. He was fascinated with the minute detailing of the things right from his childhood. Once, when he was very young, he accompanied his father on a journey to Prayaga.


He saw plentiful grains scattered along the path leading to the shore of river Ganga. It's a tradition in Hindu families to scatter the grains for the ants and birds to feed on them. Suddenly, an ant caught his attention, who was feeding on a rice grain.


He was mesmerised by this very fact that how a small grain of rice becomes food for the ant but to satisfy the hunger of a person so many grains are needed together. This incidence made him realize the importance of a single grain of rice.
ALL
Vibhu Vashisth
Vibhu Vashisth
@VIBHU_Tweet
Do you know?

The Golden Shine on the artificial jewellery of modern times is a gift by Nagarjuna.

Now,u might be wondering,who is Nagarjuna?

Nagarjuna was the name of an ancient Indian scholar&scientist whose name is associated with the fields of Alchemy,Chemistry& Metallurgy.


He conducted a number of alchemic and Metallurgical experiments directed towards transforming base elements into Gold.
According to Eduard Sachau's Alberuni's India, Nagarjuna was termed as a Metallurgist who was born 100 yrs before Alberuni's time, who was born in 973 CE.


According to some records Nagarjuna was born in 9th century CE near Somnath,Gujarat and according to some Tibetan & Chinese sources he was born in Vidarbha,Maharashtra from where he later migrated to Deccan region.


Edward Schau quotes in his book,'They have a science similar to Alchemy and Nagarjuna is famous for this science which they call as 'Rasayana'.

Nagarjuna's book 'Rasaratnakara' is one of the earliest documented texts on Alchemy,Chemistry & Metallurgy in Sanskrit.Nagarjuna successfully discovered through his experiments the alchemy of transmutation of base metals into Gold.This was not real gold but a gold like substance.
ALL
Advertisement
Aneesh Philomina Antony
Aneesh Philomina Anton...
@ProdigalTrader
If u are looking to enter a scrip just before the breakout
what would u look for?
How would u identify scrips just before breakout?

Here are a few pointers
Carefully understand the them and use at ur own discretion
(All comments r compiled from a question i posed)

1/23

Price is testing the same resistance repeatedly since long.
But now in recent times it's forming multiple candles near the same resistance with low volume.
Observe it and see for opposite move.
As soon as u get a positive candle, get in to it.

2/23

1. Volume buildup (vol > when resistance point created) along with, increase in OI, increase in delivery % on HTF
2. On LTF, price stalling around breakout area for at least 1-2hr.
3. No major events.

3/23

Stock reaching breakout LVL with HL-HH
Contraction of price and volume near it especially near 20 days ma.
If there is any breach of the latest HL it should be followed by a bullish candle with good volume.
Left side of the chart has a rally with a very steep slope of ma.

4/23

Number of wide range green bars with high volumes.
If stock is at its ATH then it adds more conviction bcz there wont b supply after that point

Consolidation below the resistance, volume of the candles & strong bullish candle closing at near highs just below the resistance

5/23
TRADING , ALL
Chuck Wendig
Chuck Wendig
@ChuckWendig
TRUMP: "I've decided to eat babies."

PEOPLE: "He can't eat babies, that's super illegal."

TRUMP, on TV, eating babies, not even cooking them first: "People are saying that I really am the best baby-eater, folks."

NYT: "Trump Vs. Babies: The Rhetoric On Both Sides Must Stop"

Jeff Flake tweets: "It is a sad day in America when this callous man can eat babies live on TV"

Jeff Flake, 10 seconds later, votes to help him install a series of 4,000 new judges that are very, very pro-babyphagia.

Judge Kavanaugh, found drunk in a Capitol Hill closet: "I LIKE BEER. OKAY? I LIKE BEER AND I LIKE BABIES AND THAT'S NOT SO WRONG. THAT'S RIGHT. I LIKE BABIES DIPPED IN BEER. I JUST DIP EM. IN THE BEER. THE BABIES. THE BABIES I EAT. OKAY? THAT'S AMERICA."

Mike Pence, wringing his hands about the baby-eating: "We must take care of our children. Our youngest must be protected on this troubling day."

Mike Pence then invites a Baby Chef to say a prayer.

Twitter Users: "It's a distraction technique! Trump eating babies is trying to DISTRACT YOU from ALL THE OTHER HORRORS, which are themselves distractions from the BABY-EATING. It's DISTRACTIONS ALL THE WAY DOWN."

Trump: *eats tons of babies at a rally*

*literal tons of babies*
POLITICS
Professor
Professor
@DillikiBiili
So friends here is the thread on the recommended pathway for new entrants in the stock market.

Here I will share what I believe are essentials for anybody who is interested in stock markets and the resources to learn them, its from my experience and by no means exhaustive..

First the very basic : The Dow theory, Everybody must have basic understanding of it and must learn to observe High Highs, Higher Lows, Lower Highs and Lowers lows on charts and their

Even those who are more inclined towards fundamental side can also benefit from Dow theory, as it can hint start & end of Bull/Bear runs thereby indication entry and exits.


Next basic is Wyckoff's Theory. It tells how accumulation and distribution happens with regularity and how the market actually

Dow theory is old but
SCREENERS , ALL , MARKETESSENTIALS