Some of my machine learning books.

Interviews aren't only about technical skills.

Here are some questions to help you prepare.

🧵👇

Explain what you have been working on for the past few weeks.

What are the most exciting parts about that work?

What portion of it do you consider boring and why?

[2 / 10]

What specific libraries and frameworks are you familiar with?

What's the minimum set of libraries and frameworks that you'd recommend to any practitioner?

[3 / 10]

What kind of problems have you worked on in the past?

Can you list the specific use cases related to each one of these?

[4 / 10]

What is the most exciting project you have ever worked on?

What was your role and responsibilities on that project?

Why do you think your work on that project was important?

[5 / 10]

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.

(2 / 16)

Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

(3 / 16)

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.

(4 / 16)

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.

(5 / 16)

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?

👇

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.

↓

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

↓ 1/5

"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

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
Shri Rama shares with Hanuman the Abhignana Vaakhyam, the words that would enable him to gain the confidence of Mata Seetha.

Hanuman stands leaning a little towards the left, showing how he hear Shri Rama's words and also assuring him Your Wish is my Command.
The Vigraham of Hanuman gives us the Strength, Confidence and Bhakthi.
There is a place marked here to show Hanuman’s leap to Lanka over sea.

Devotees run and leap over the stone for better health.Aval Naivedhyam is famous here. Those children who have nightmares are told to pray Alathiyur Hanuman. Mantra 👇

Alathiyur Hanumane
Pedi Swampnam kanarudhe
Pedi Swapnam Kandalo
Val Kondu Thattiyunarthename
🙏🕉🙏

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.

RISHI PINGALA:THE ANCIENT INDIAN SAGE DISCREDITED FOR HIS MAJOR CONTRIBUTIONS TO MATHEMATICS & PHI

Have you ever heard about,"The Golden Ratio"?
The Golden Ratio or Phi(Greek) is a mathematical ratio of nature. Mother Nature is the most exquisite artist in herself.

&Its this Phi she uses to create all the beauty in World.
If anyone uses this ratio correctly in their art,it can turn into a spell-binding piece of work.Such is the significance of'Phi'.But my thread isn't about it.Its about somethin called'Fibonacci Series'&who actually made it

Phi is related to Fibonacci series(0,1,1,2,3,5,8,13,21,34...)
&this series is responsible for everything dat is beautiful on this planet. Just try to calculate the ratio of any 2 successive numbers ÷ each of them by the number before it in above infinite series.

E.g. 3/2=1.5, 5/3=1.666, 8/5=1.6, 34/21=1.61904 and so on. Did you notice something? The ratio is coming almost same every time. This ratio is called 'Phi 'which is numerically represented as 1:1.618. This Golden Spiral is made in exact proportion of 1:1.618.

The thing I want to emphasize here is that we always find only those things appealing and attractive that have Phi. It's in our subconscious. Isn't it some revelation? We are the part of this Divine proportion of the God or the Mother Nature. Check these:

You can lose 40 pounds of fat,

And be in incredible shape in 4 months.

All you have to do is follow these 11 simple guidelines:

· Preparation meets expectations

What do you expect to be feasible for you? Can you let go of the things that got you here?

Never treat such an endeavor as a sprint. It's a marathon.

Set realistic goals.

There's only ONE shortcut to get there faster: DO THE WORK!

· Avoid processed food

Eating highly processed foods for just two weeks can lead to weight gain and body fat increase.

Eat real food.

Get 90% of your calories from single-ingredient nutrient-dense

· Studies indicate that a high-protein diet is essential for stimulating muscle growth and losing fat.

0.73–1 gram of protein per pound (1.6–2.2 g/kg) of body weight per day is best for maximizing muscle gain and losing that stubborn fat.

· Never trust your gut

Weigh and track your foods. Timestamp your eating habits.
Compare prices between going out and prepping at home.

This will give you actual numbers and the right education to move forward from being unaware to being actually aware.

@45RapeKatieJohn @buzz\_chronicles @buzz\\\_chronicles @buzz_chronicles save as category @pdfmakerapp grab @pdfmakerapp grab this @readwiseio save @readwiseio save thread @threader compile summarize @Threadreaderapp unroll please @ttttreads unroll @unrollthread.com @SaveToBookmarks