My recommendations to start with machine learning math:

• Statistics 110: Probability — Harvard University
• Essence of Linear Algebra — 3Blue1Brown
• Multivariate Calculus — Coursera

Links and 3 additional free resources here: https://t.co/fEmpmRKEvJ

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."

👇

What's machine learning?

Not a framework. Not a different language. Not the latest "cool" thing around the Internet.

Thread: Let me convince you that machine learning is the best thing since sliced bread 🍞.

Let's start with a simple exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function. It should be simple, right?

To avoid using a specific syntax, let's represent this function as follows:

y = f(X)

Where:
• X → The input number
• y → The result (0 if even, 1 otherwise)
• f → The function that does the work

We can now get to the interesting part.

If you are a programmer, writing this function is simple.

You know the relationship between X (the input number) and y (the output), so you can easily write the rules (the function.)

In machine learning, we don't write the rules. We learn them.

Imagine we had an algorithm that processes many samples (X → y), learns the relationship between them, and comes up with the correct rules.

We call these samples "training data," and they would like like this:

• 2 → 0
• 5 → 1
• 8 → 0
• 4 → 0
• 3 → 1
• ...

One of the most popular activation functions used in deep learning models is ReLU.

I asked: "Is ReLU continuous and differentiable?"

Surprisingly, a lot of people were confused about this.

Let's break this down step by step: ↓

Let's start by defining ReLU:

f(x) = max(0, x)

In English: if x <= 0, the function will return 0. Otherwise, the function will return x.

If you draw this function, you'll get the attached chart.

Notice there are no discontinuities in the function.

This should be enough to answer half of the original question: the ReLU function is continuous.

Let's now think about the differentiable part.

A necessary condition for a function to be differentiable: it must be continuous.

ReLU is continuous. That's good, but not enough.

Its derivative should also exist for every individual point.

Here is where things get interesting.

We can compute the derivative of a function using the attached formula.

(I'm not going to explain where this is coming from; you can trust me on this one.)

We can use this formula to see whether ReLU is differentiable.

11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

🧵👇

1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.

2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.

3. Write a function that finds the duplicate number in an unsorted array containing every number from 1 to 100.

Only one of the numbers will appear twice.

4. Write a function that removes every duplicate value in an array.

There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.

How you can filter stocks for Intraday trades - 🧵

As a kid, we would do homework before school the next day - you have to do homework here too. A specific sector performs on a particular day and studying things a day before will help you spot that particular sector. (1/11)

To prepare for the next trading day, you got to check the Index charts of all sectors. You got to keep an eye on a specific Index if you see a few factors:

- If any Index closed exactly at the resistance, you can get a trade on either side on the next day. (2/11)

- If it gave a rejection candle at resistance.

- If any Index closed exactly at support, you can get a trade on either side the following day.

- If there was a bullish signal at the support.

- If any Index gave a breakout or breakdown and then gave a good closing. (3/11)

- If any Index has taken support at 50 or 200 EMA.

Once you spot the Index, look at all the stocks in that particular Index. Pick at least 3-4 stocks from that Index and add them to your watchlist for the next day. It's ideal to check both higher and lower timeframes. (4/11)

What you should do next day? (When I am unable to check Indices and stocks a day before, I sometimes directly start with this process.)

- Start with checking the top gainers/losers list. I observe market from 9:15 to 9:45am to check the sectors performing well on that day.(5/11)

Please please please take your cyber security seriously even if you don’t consider yourself a high risk person. Here are a few easy things to start with. 1/n

Switch from WhatsApp to Signal and enable disappearing messages. Most of us cannot stop using WhatsApp entirely but at least turn off back ups to Google Drive or iCloud. 2/n

Do not have private conversations using the messaging service on any social media platform like Facebook, Instagram or Twitter. 3/n

Switch from Gmail to ProtonMail or any other secure email service provider. 4/n


Switch from Google Docs to CryptPad or RiseUp Pad.

Stories of Guruvayurappan
Shri Krishna Leela

This is a beautiful story of Guruvayurappan. Once there lived in Guruvayur a poor family. Kitta, his wife & kids were struggling for their daily needs. With only a coconut tree in house, he earned a living by selling coconuts.

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

It was Utsavam time in Guruvayur. From far behind kitta and family worshipped Guruvayurappan. Annadanam was given daily and no one was left empty stomach. Only Kitta didn't receive. That night, it was only Kitta's family who was sleeping without food. After temple nada closed

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.

@buzz_chronicles @AchuthArora @Abhirajputfit @rattibha Thanks bro!

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