A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓

I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."

This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.

My recommendation to start learning how to deal with structured data: Kaggle's Pandas Tutorial.

• Creating, Reading, Writing
• Indexing, Selecting, Assigning
• Summary Functions, Maps
• Grouping, Sorting
• Data Types, Missing Values
• Renaming, Combining

100% Python 🐍.

An introduction to one of the the most basic structures used in machine learning: a tensor.

🧵👇

Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

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Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

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The rank of a tensor refers to the tensor's number of axes.

Examples:

▫️ The rank of a matrix is 2 because it has two axes.
▫️ The rank of a vector is 1 because it has a single axis.

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The shape of a tensor describes the number of dimensions along each axis.

Example:

▫️ A square matrix may have (3, 3) dimensions.
▫️ A tensor of rank 3 may have (2, 5, 7) dimensions.

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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.

ULTIMATE GUIDE TO 21 and 200 Moving Average
SUNDAY THREAD 🔖
A trend Following System That will Help You Improve Trading immensely.

RETWEET AND SHARE 🔄
@kuttrapali26 @AmitabhJha3
#stocks #StockMarket
1/n

Moving averages
EMA and SMA
1. Exponential Moving Average (EMA) is similar to Simple Moving Average (SMA), measuring trend direction over a period of time.
2. However, whereas SMA simply calculates an average of price data, EMA applies more weight to data that is more current
2/

21 EMA : SHORT TERM TREND
1. Calculates the Average Price of past 21 days
2. A stock above 21 ema is in strong momentum , which will give pullbacks to 21 ema again and again.
3. Stock above 21 ema should never be shorted so as to stay with the trend
3/n

3. A stock which moves 10-15% above 21 ema will be too extended and hence will generally pullback
Stocks take dynamic support on 21 ema
4. You can Short only if its too extended like 15 to 20% from 21 ema ( Not advised )
#ADANIPOWER:
#pnbhousing
4/n

1. Whenever 21 ema is sloping upwards its called uptrend
2. Whenever 21 ema is sloping downwards its called dowtrend
3. Whenever 21 ema is sideways its called consolidation
⏫Buy in uptrend
⏬Sell in downtrend
Examples:
5/n

Strong Monopolies:

IRCTC 100% Market share in Rail Network.

IEX >90% market share in power trading.

Zydus wellness >90% market share in sugar free product.

Eicher motors >85% market share in 250cc bikes category.

MCX >85% market share in commodity trading.

Coal India >80% market share in coal production in India.

ITC >75% market share in cigarettes.

Honda Siel >75% in portable power generators.

Hindustan Zinc >75% market share in primary zinc industry.

Asahi India Glass >70% market share in automotive glass.

NRB Bearings >70% market share in needle roller bearings.

Pidilite >65% market share in adhesives.

CAMS >65% market share in RTA within mutual fund industry.

Time Technoplast >65% market share in polymer based industrial packaging.

Concor >65% market share in domestic container cargo transport.

Exide >60% market share in lead batteries.

Naukri >60% market share in the Indian job market space.

Praj >60% market share in ethanol plant installing.

Indiamart Intermesh >55% market share in the online B2B Classified space.

Borosil Renewables >55% market share in Lab glass.

Vst Tillers >50% market share in
Power tillers.

Delta corp >50% in online poker games.

Vinati Organics >50% market share in IBB.

RANI GAIDINLIU:THE LESSER KNOWN FREEDOM FIGHTER

Rani Gaidinliu joined the freedom struggle at 13 &was sentenced to life imprisonment at 16."We r free people—the White should not rule over us.”Thus calling out to her Zeliangrong tribesmen was the voice of 13yr-old Rani Gaidinliu.

She joined the freedom struggle in the present-day Northeast India. Today, her struggle is lost in history as most of her countrymen wouldn’t even have heard her name. B'coz our history textbooks tell us that only Mahatma Gandhi &Pandit Nehru got India its Independence.

At 16 yrs of age, this valiant girl was sentenced to life imprisonment for leading a movement to drive out the colonial rulers from Manipur and Nagaland. Nevertheless, she was released in 1947 after India’s independence and was given the title ,"Rani of Nagas".

EARLY LIFE AND HERAKA MOVEMENT

Gaidinliu belonged to the Rongmei clan of the Zeliangrong tribe in the Tamenglong district of western Manipur. Born on January 26, 1915, she was fifth among eight children.

Her native village was Nungkao, which is present day Tamenglong distt. Brought up in a specially backward region,she couldn't receive any formal education. However,this didn't stop her from joining the freedom struggle. At the age of 10 she came under the influence of her cousin.

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