BC MACHINE LEARNING

Saved by @Mollyycolllinss

Pratham Prasoon
@PrasoonPratham 3 years, 4 months ago 548 views

Save to PDF Share See On Twitter

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

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👇

(2 / 17)

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 / 17)

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

(4 / 17)

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

It is 100% compulsory.

(5 / 17)

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?

Advertisement

Pratham
@PrasoonPratham

There are a million machine learning tutorials and courses out there which makes it really hard to choose the right one for you.

Here are some that I really like that you might find useful.
🧵 👇🏻

Before we begin, here's some unsolicited advice.

It is not about how many courses you take that matters but the problems that you solve with your knowledge.

You can take any combination of courses and tutorials from this list that you think will be useful.

Python crash course by Traversy Media (YouTube)

• Introduces you to a lot of basic concepts in Python
• Only 1.5 hours long

Intermediate Python course (YouTube)

• Helps you understand some of the more difficult concepts in Python

The neural networks series by 3blue1brown (YouTube)

• Visually explains neural networks
• Short and concise

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)

$Pratham Prasoon \U0001f680$

Pratham Prasoon 🚀...
@PrasoonPratham

Here's how I got started with my first machine learning project and you can too.
Let's take a look.

(This thread will take from zero to being a hero in machine learning, trust me)

(1 / 22)
🧵👇

Getting started with your first machine learning project might actually much easier than it seems, if I can do it, certainly anyone can.

I did not use:
- Any Math
- An expensive computer
- Complex programming concepts

(2 / 22)

Here's what I did use:

- A free GPU on Google Colab
- Python
- TensorFlow
- Numpy
- Pandas
- Kaggle
- Scikit-Learn
- Google
- StackOverFlow

(3 / 22)

This project was actually a Kaggle challenge based on the MNIST dataset which is a collection images of 70,000 hand written digits.

You can find the dataset here👇
🔗//kaggle.com/c/digit-recognizer

(4 / 22)

Before we go over the code of this project, it is highly reccomended that you complete this free course on YouTube👇

Machine Learning foundations course
🔗//youtu.be/_Z9TRANg4c0

Code👇
🔗//colab.research.google.com/github/PrasoonPratham/Kaggle/blob/main/MNIST.ipynb

(5 / 22)

More from Machine learning

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 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)

Advertisement

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."

👇

$Fionna O'Leary, \U0001f56f\U0001f1ea\U0001f1fa$

Fionna O'Leary, 🕯🇪🇺...
@fascinatorfun

Thanks for this incredibly helpful analysis @dgurdasani1

Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published

SUMMARY: the Oxford/Astra trial examined dosing with gaps between 4-12 wks- although longer gaps appear to be limited mostly to younger participants. There was no difference reported in published data between these & efficacy from the 1st dose seems high for severe disease.
— Deepti Gurdasani (@dgurdasani1) December 31, 2020

Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?

Not to mention the efficacy data of the full first dose over that same period?

I’d quite like to know whether I am to be a guinea pig & the ongoing risks to manage

You attached photos of excerpts from a paper. Could you attach the link?

Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.

Discussions of 1 vs 2 doses suggest many are not aware of Pfizer's trials which evaluated 1 vs 2 dose immunogenicity, assessed multiple formulations (BNT162b1 BNT162b2 etc) & conducted dose-ranging in both young & old adults at the start. Saw "clear benefit of booster at day 21" pic.twitter.com/mpyxu9xFSF
— Dr Nicole E Basta (@IDEpiPhD) December 31, 2020

Here’s the link to the

I’ve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.

$Unknown \U0001f464$

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

You May Also Like

$\U0001f1fa\U0001f1f8 Jules \U0001f1fa\U0001f1f8$

🇺🇸 Jules 🇺🇸...
@mayweliveasONE

2021 Predictions‼️

Made by James Howard Kunstler

👇🏻👀

Kunstler is said to be one of the best thinkers and authors of our modern era. He’s well known as an expert in energy economics, among other topics.

He is NOT a fan of Trump which makes his predictions even more interesting.

1) The election is re-adjudicated, fraud subtracted from the tally, and President Trump is declared the winner.

2) The mail-in vote for the Georgia Senate seat runoff is disqualified as systematic fraud is revealed. Stacy Abrams is indicted for organizing the fraud.

3) A number of political celebrities, DC swamp rats, K-Street hustlers, media figures, and tech company executives are arrested and charged with serious crimes around election fraud.

4) The CIA is purged and reduced to a strictly analytical role for advising the executive.

$Peggy Klaver #FBR \U0001f30a\u270a\U0001f3fb\u270a\U0001f3fd\u270a\U0001f3ff\u270a\U0001f3fc$

Peggy Klaver #FBR 🌊✊🏻✊...
@Pegerella

The man who invented autocorrect should burn in hello.

It was meant to put a smile on your face. Please stop sending me instructions on how to turn my autocorrect off. 😳🤔

Texted my friends to ask if I could take their kids with me to Satan... still have not heard back from them. I meant Santa...

Thanks for all the love, I appreciate a follow. 💗

Advertisement

Techno Funda Investing
@TechnoFunda6

Thread 🧵explaining #ROE & #ROCE and its use in #FundamentalAnalysis

(Must read for #FA learners)

🙏Like & RT to spread learning with all 🙏

Views Welcome 👉 @dmuthuk @Vivek_Investor @datta_arvind @caniravkaria @FI_InvestIndia @AdityaD_Shah

#FundamentalAnalysis #StockMarket

1⃣

Return on Equity (#ROE) and Return on Capital Employed (#ROCE) are two important Financial Ratios used in #FundamentalAnalysis of Stocks.

#StockMarket #Investing

2⃣

Return on Equity (ROE) is the ratio of Net Income (Net Profit) of a company and it’s Total Equity (Shareholder's Equity).

Here - Shareholders Equity = Share Capital + Reserves & Surplus.

#StockMarket #Investing #FundamentalAnalysis

3⃣

#ROE is a measure of how much net profit a company is making for every rupee invested by its shareholders in the company.

It can be said that higher the ROE, better it is for the equity shareholders. It tells shareholders about how effectively their money is being used.

4⃣

However, you should not trust high ROE blindly. Biggest drawbacks or weaknesses of ROE is that it completely ignores debt.

Hence for companies having high levels of debt, ROE will give a higher but misleading value & therefore we need to look at #ROCE along with #ROE.

Vibhu Vashisth
@VIBHU_Tweet

Did you know?
Ahilyabai Holkar was one of India's
greatest women rulers of all time.

Ahilyabai was Born on May 31,1725 in the village of Chondi,Jamkhed distt,Maharashtra to a man named Mankoji Shinde,who was the Patil(Chief) of a village and a member of proud Dhangar Community.

Despite the fact that girls' education was a farther dream in those days, Ahilyabai was homeschooled by her father.
Ahilya didn't belong to any royal family but in a twist of fate she is still remembered as one of the most revered Queen in the history.

While they were living their humdrum lives, one day Malhar Rao Holkar, commander of the Peshwa Bajirao & Lord of the Malwa stopped in Chondi on his way to Pune, where he couldn't help but notice a young 8yrs old Ahilyabai in the temple service feeding the poor & hungry.

He was so impressed by the sheer innocence&beauty with simplicity character of Ahilyabai dat he asked her hand in marriage for his son Khanderao Holkar.Just like that she got married to Khanderao in 1733 at a tender age of 8&became a bride of esteemed Maratha community of Holkars

But destiny had something else in store for her as her husband died in the Battle of Kumbher in 1754 leaving her widow at 29.Distressed Ahilyabai was to commit Sati when her father-in-law Malhar Rao refused to let it happen.

Amit Schandillia
@Schandillia

[THREAD: HISTORY OF THE HINDU TEMPLE]
1/102
Four hours south of Aswan in Lower Nubia, right by the Nile, is a region dotted with temple complexes built more than 3,000 years ago. Some like the temples of Amada, Derr, and Beit el-Wali go as far back as 1500 BC.

2/102
A little further south, not far from the border with Sudan, is another complex dedicated to Ramses II, Hathor, and Nefertari in the village of Ipsambul, better known locally as Abu Sunbul and internationally as Abu Simbel. These go back to the 13th century BC.

3/102
Djeser-Djeseru, also known as the mortuary temple of Hatshepsut in Upper Egypt also dates to 1400 BC. Over 500 years older is the nearby Mentuhotep II, also a mortuary temple. Both were dedicated to the cult of Amun Ra, besides their respective pharaohs.

4/102
Far older is the Ġgantija complex of Malta. Going as far back as 3600 BC, this one comprises of two temples entirely made of stone and enclosed in a stone wall. From the figures therein, it's understood to be a site of some kind of a fertility cult worship.

5/102
Dating from the same period are the limestone megaliths of Ħaġar Qim and Mnajdra, both said to have once hosted ritualistic sacrifices as part of the same fertility cult worship as found in the Ġgantija complex. Malta has some of the oldest temple complexes in Europe.