BC MACHINE LEARNING

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Pratham Prasoon
@PrasoonPratham 4 years, 5 months ago 733 views

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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.

(3 / 11)

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)

Once the dials are well tuned, you have a well trained neural network!

Each dial's numeric value is dependent on a "weight" and a "bias". The weight determines how important the neuron is and the bias make it flexible.

(6 / 11)

So here's a recap of what we've looked at so far:

The neural net is the brain of the machine learning model, the dials you have to adjust to make that neural net work are the neurons.

(7 / 11)

Each time data passes through the neural network, we get to know how wrong it is. The measure of how wrong a neural network is called the "loss". The neural network uses this thing called an "optmizer" to reduce "loss" and tries to get less wrong after each iteration.

(8 / 11)

The number of times the data passes through the neural net is called the "epoch".

Let's summarize the entire thing👇

(9 / 11)

Neural Network: The brain of our machine learning model
Neuron : Each dial in a neural network
Weight : How important the neuron is
Bias : Flexibility of neuron

(10 / 11)

Epoch : Number of times the data passes through the neural network
Loss : How wrong the neural net is
Optimizer : Tries to reduce loss and make the neural net less wrong

Congrats! You know a fair bit about commonly used machine learning terms 😉
(11 / 11 🎉)

More from 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

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)

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Pratham Prasoon
@PrasoonPratham

Machine Learning for the Web developer in 2021.

The beginner's guide.

🧵👇

I started machine learning as a web developer, if I can do it then anyone can.

This carefully curated thread will give you key insights into my journey and how you can make this transition, seamlessly.

(2 / 14)

"Machine learning is not what you think it is"

One of the main reasons why people find it difficult to get started with machine learning is because of the lack of information, and rightfully so.

(3 / 14)

Machine learning as a concept has existed since the 1950s, but has only become popular in recent years because of the exponential rise of advancements in computer hardware.

(4 / 14)

In short, it because of the sudden rise of this technology ,which was previously unknown to the general public, that there is a lot of misinformation around it.

(5 / 14)

Pratham
@PrasoonPratham

Everyone tells you how important math is in machine learning.

But I believe that if you want to learn machine learning today, there are things more important than math which very few people will tell you about.

Here are 5 of them.

🧵 👇🏻

The field of machine learning has been very academic which is why there is so much emphasis on learning math for it.

But, today with the frameworks that we have, not knowing how to multiply matrices is far less of an issue than knowing how to use the tools in this list 👇🏻

Yes, I know; you have R, Julia, and whatnot for machine learning but as of today, Python is the industry standard.

Get your data structures and algorithms right in Python because you will need them, here are some concepts worth learning 👇🏻

Let's say you're building the next big GPT-3 powered app that's going to take over the world.

You add a new feature to your app but it breaks the entire thing, what do you do now?

Learn Git, the ultimate version control tool.

I use this cheat sheet for all my git needs 👇🏻

You will be constantly be working on multiple packages which specific python version requirements 🐍

Virtual environments like Anaconda or Poetry will make this process much easier for you.

Who likes to see broken dependencies while running code?

Pratham Prasoon
@PrasoonPratham

Which libraries do you really need to get started with Machine Learning and why?

🧵👇

First and foremost, make sure that you have nailed the fundamental concepts of Python because machine learning requires a lot of programming!

(2 / 19)

If you know these topics, then you are good to go with machine learning in Python

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

(3 / 19)

Now, let's understand what popular python machine learning libraries do.

We will talk about👇
- TensorFlow (+ Keras)
- PyTorch
- Pandas
- Numpy
- Matplotlib
- SciKit Learn
- Seaborn

(4 / 19)

Now let's cover the libraries that you have to learn (at least the basics) for machine learning

1. Pandas

Pandas is a python library that allows you to store and read data from spreadsheets ( .csv, .xlsv files ) in structures called Dataframes.

(5 / 19)

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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)

Mads Brodt
@madsbrodt

I've applied for a total of 10 development jobs in my life:

- 7 lead to interviews
- Of those 7, 4 was a good fit for me
- Of those 4, 3 offered me the job
- In all 3 cases, I negotiated a better deal

🧵 on what worked for me and what YOU can do to get similar results 👇

1. Apply for the RIGHT jobs. Look for midsize to large companies, not necessarily tech related. These tend to get way fewer applications than traditional tech companies, so it's easier to get your foot in the door - and they also tend to be great environments for learning.

2. Spend time on your application. Make sure your resumé and written application is tailored to the exact position. Recruiters or hiring managers can smell a copy/pasted resumé from miles away. Use as many words from the job spec in your application as possible.

3. Have a portfolio. Show off your skills and some personality on your website. This is a nobrainer for front-end devs, but it's true for other areas too. It's a great way to stand out over other candidates by actually *showing* the value you can create and your experience.

4. Establish personal contact. Send a quick message or make a phone call before applying. Just introduce yourself to the person in charge of hiring and ask a simple question. It's the best way I find to make them remember your application when you submit it.

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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."

👇

IM POCUS Focus
@IMPOCUSFocus

A tweetorial on optimal machine settings for B line image acquisition, with a focus on: focal position at pleura, harmonics off, and increased far gain.

Inspired/based on the recent article - https://t.co/vv2xBSZm6N
@cameron_baston @IsaacMatthiasM1

#POCUS #IMPOCUS

1/18

Let's start with some polls
We will stick to low frequency probes here (i.e. curvilinear and phased array - linear has its own purpose for detailed pleural assessment)

Which preset do you prefer for B line assessment?

2/

What depth do you typically set?

3/

After selecting preset and selecting depth, do you ever adjust any other machine settings to optimize B line assessment?

4/

Importantly, there is no right answer to the above questions!

We won't discuss much about preset or probe type. But we will discuss machine settings to optimize B line quality, based on
1) Theory based on physical basis of B lines
2) In vitro study
3) Study in patients

5/

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.

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I'm going to attempt to chronologically list all of the Marvel characters Stan Lee had a hand in creating. Working with his genius collaborators, Stan and his team produced an insane amount of iconic characters in a roughy ten year period. (Thread)

With his younger brother, writer Larry Lieber, and Jack "the King" Kirby:

Groot

Tales to Astonish #13 (November, 1960)

With writer Lieber, and artist Kirby:

Fin Fang Foom

Strange Tales #89 (October, 1961)

With the King, Stan gives birth to the Marvel Universe:

The Fantastic Four

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With Jack Kirby:

Mister Fantastic

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Damodar Hegde
@DamodarHegde4

MUST READ TILL END:
FB POST BY MR PRABEER BASU,
IAS-1976(BIHAR)
" Received a hurtful post from an old friend and colleague. He was on my daily Broadcast list of WhatsApp. He requested that I do not send him any of my posts on 'political' issues. I gave him a long explaination
>2

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on why I write and what I write. But he was not convinced.

Being alerted by him thus, I felt I should clarify to all who read my posts on what exactly I think and do.

The charge against me is that I wrongly support our current Prime Minister, Shri Narendra Modi..
>3

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and that it establishes I am not rational and impartial anymore.
Let me try and explain.First and foremost,I do not write on 'political' issues. I write on 'national' issues. For example, I have no opinion on whether Rahul Gandhi should be President of his party or not.
>4

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Similarly, I have no views whether BJP is a better party than Congress or vice versa.
Even after 36 years in the IAS I have no MLA, MP or anyone involved in politics whom I can classify as my friend or even acquaintance.
>5

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So, naturally I don't care which party wins or loses as long as the winning party runs an honest government doing good to my nation.
Now, why I speak in favour of Shri Modi:

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Noah Smith
@Noahpinion

1/OK, data mystery time.

This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE

That's weird. Income inequality in China is legendary.

Let's check this number.

2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".

The report is available here:

3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.

The graph cites the World Development Indicators as its source for the income inequality data.

4/The World Development Indicators are available at the World Bank's website.

Here's the Gini index: https://t.co/MvylQzpX6A

It looks as if the latest estimate for China's Gini is 42.2.

That estimate is from 2012.

5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.

I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.

Vibhu Vashisth
@VIBHU_Tweet

प्राचीन काल में गाधि नामक एक राजा थे।उनकी सत्यवती नाम की एक पुत्री थी।राजा गाधि ने अपनी पुत्री का विवाह महर्षि भृगु के पुत्र से करवा दिया।महर्षि भृगु इस विवाह से बहुत प्रसन्न हुए और उन्होने अपनी पुत्रवधु को आशीर्वाद देकर उसे कोई भी वर मांगने को कहा।

सत्यवती ने महर्षि भृगु से अपने तथा अपनी माता के लिए पुत्र का वरदान मांगा।ये जानकर महर्षि भृगु ने यज्ञ किया और तत्पश्चात सत्यवती और उसकी माता को अलग-अलग प्रकार के दो चरू (यज्ञ के लिए पकाया हुआ अन्न) दिए और कहा कि ऋतु स्नान के बाद तुम्हारी माता पुत्र की इच्छा लेकर पीपल का आलिंगन...

...करें और तुम भी पुत्र की इच्छा लेकर गूलर वृक्ष का आलिंगन करना। आलिंगन करने के बाद चरू का सेवन करना, इससे तुम दोनो को पुत्र प्राप्ति होगी।परंतु मां बेटी के चरू आपस में बदल जाते हैं और ये महर्षि भृगु अपनी दिव्य दृष्टि से देख लेते हैं।

भृगु ऋषि सत्यवती से कहते हैं,"पुत्री तुम्हारा और तुम्हारी माता ने एक दुसरे के चरू खा लिए हैं।इस कारण तुम्हारा पुत्र ब्राह्मण होते हुए भी क्षत्रिय सा आचरण करेगा और तुम्हारी माता का पुत्र क्षत्रिय होकर भी ब्राह्मण सा आचरण करेगा।"
इस पर सत्यवती ने भृगु ऋषि से बड़ी विनती की।

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Pegasus of Dharma
@HarduttSuhas

The BATTLE OF PAVAN KHIND (13 July 1660)

300 MARATHAS against 10000+ ADIL SHAHI TROOPS

A Sparta style last man stand at a mountain pass which ensured Shivaji Maharaj's safety.

Valour, loyalty and sacrifice!

Baji Prabhu Deshpande was one of the most able lieutenants of Shivaji. He sacrificed his life defending Shivaji in a heroic last stand known as the "Battle of Pavan Khind" in which his force was hugely outnumbered.(1)

In this battle he became a martyr, but managed to halt the enemy forces for several hours, which secured Shivaji’s safety and victory on another front. Without this heroic last stand Shivaji’s glorious career may have been cut short.(2)

Baji Prabhu was senior to Shivaji. In the beginning he opposed Shivaji being from a rival Maratha clan, but realised that Shivaji stood for national resurgence. Henceforth he became a friend and loyal supporter of Shivaji.(3)

In 1660, Shivaji was trapped in the fort of Panhala, under siege and vastly outnumbered by an Adilshahi army led by general Siddhi Jauhar.(4)