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

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Pratham
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A git cheat sheet for all your needs!

https://t.co/H7QdOrthwV
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Pratham Prasoon
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@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
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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
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
This is a step-by-step guide on getting started with Python as a total beginner.

(includes free resources and everything else you need to get started)
🧵👇

Before we begin, I want to congratulate you on your decision to learn how to code using Python.

I still remember how I wrote my first piece of code 6 years and all the amazing and cool things I've been able to do with it ever since.

(2 / 24)

Make sure to keep in mind that it is probably best for you to keep your expectations in check.

Don't expect to make AAA games or state of the art machine learning models in a week.

(3 / 24)

Programming is not something that you can learn in a single week, it takes consistent effort and dedication over time to get good at it.

With all that being said, let's dive straight in.

(4 / 24)

In order to write Python code, you'll need to install Python on your system.

Linux and macOS users can skip this step because they come pre-installed with Python.

Download link: https://t.co/KSZ4Qd6CNk

(5 / 24)
TECH
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

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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?

👇
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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
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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
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
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

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The UN just voted to condemn Israel 9 times, and the rest of the world 0.

View the resolutions and voting results here:

The resolution titled "The occupied Syrian Golan," which condemns Israel for "repressive measures" against Syrian citizens in the Golan Heights, was adopted by a vote of 151 - 2 - 14.

Israel and the U.S. voted 'No' https://t.co/HoO7oz0dwr


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सा विद्या या विमुक्तये...
@raghav_aanya
कुंडली में 12 भाव होते हैं। कैसे ज्योतिष द्वारा रोग के आंकलन करते समय कुंडली के विभिन्न भावों से गणना करते हैं आज इस पर चर्चा करेंगे।
कुण्डली को कालपुरुष की संज्ञा देकर इसमें शरीर के अंगों को स्थापित कर उनसे रोग, रोगेश, रोग को बढ़ाने घटाने वाले ग्रह


रोग की स्थिति में उत्प्रेरक का कार्य करने वाले ग्रह, आयुर्वेदिक/ऐलोपैथी/होमियोपैथी में से कौन कारगर होगा इसका आँकलन, रक्त विकार, रक्त और आपरेशन की स्थिति, कौन सा आंतरिक या बाहरी अंग प्रभावित होगा इत्यादि गणना करने में कुंडली का प्रयोग किया जाता है।


मेडिकल ज्योतिष में आज के समय में Dr. K. S. Charak का नाम निर्विवाद रूप से प्रथम स्थान रखता है। उनकी लिखी कई पुस्तकें आज इस क्षेत्र में नए ज्योतिषों का मार्गदर्शन कर रही हैं।
प्रथम भाव -
इस भाव से हम व्यक्ति की रोगप्रतिरोधक क्षमता, सिर, मष्तिस्क का विचार करते हैं।


द्वितीय भाव-
दाहिना नेत्र, मुख, वाणी, नाक, गर्दन व गले के ऊपरी भाग का विचार होता है।
तृतीय भाव-
अस्थि, गला,कान, हाथ, कंधे व छाती के आंतरिक अंगों का शुरुआती भाग इत्यादि।

चतुर्थ भाव- छाती व इसके आंतरिक अंग, जातक की मानसिक स्थिति/प्रकृति, स्तन आदि की गणना की जाती है


पंचम भाव-
जातक की बुद्धि व उसकी तीव्रता,पीठ, पसलियां,पेट, हृदय की स्थिति आंकलन में प्रयोग होता है।

षष्ठ भाव-
रोग भाव कहा जाता है। कुंडली मे इसके तत्कालिक भाव स्वामी, कालपुरुष कुंडली के स्वामी, दृष्टि संबंध, रोगेश की स्थिति, रोगेश के नक्षत्र औऱ रोगेश व भाव की डिग्री इत्यादि।
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Alathiyur Hanuman Temple
Malappuram, Kerala 🚩
#keralatemples

One of the Powerful Anjaneya Temples in Kerala. Hanuman temple is adjacent to Shri Ramar Sannidhi. Here Hanuman's head is tilted towards the left to hear Shri Rama's words. Shri Rama is entrusting Hanuman👇


Task of locating Mata Sita in Lanka.

Prathishta of Hanuman is done by Sage Vasishta. Eventhough this is Shri Ram Temple, importance is given to Hanuman
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