Date - 23-12-23
Company: Splunk
Role: Data Scientist


1. How Are Weights Initialized in a Neural network?

Ans: There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.

Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.


2. What are the variants of Gradient descent?

Ans: Stochastic Gradient Descent: We use only a single training example for calculation of gradient and
update parameters.
Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at
each iteration.
Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of
Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is
used.


3. What are the feature selection methods used to select the right variables?

Ans: There are two main methods for feature selection:
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is "bad data in, bad answer out." When we're limiting or selecting the features, it's all about selecting the useful feature.

Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together. Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.

4.  What is joint sampling and separate sampling?

Ans:
· Joint sampling is done when there are equal number of events and non-events. Not appropriate for imbalanced data
· Separate sampling is done for imbalanced data. For rare event, all observations are kept when target = 1 and only few observations are kept when target = 0.

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Stay Safe & Happy Learning💙

Google is back with their STEP Intern program.

👉Batch eligible: Students currently in 2nd year of college (2026 grads)

⏳ Duration: 10-12 weeks

🎯Link: https://lnkd.in/djscq3-d

🕰 Last Day to apply: 19th Jan, 2024

Company Name: Dassault Systems

Role: Apprentice - Web Development

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Apply: https://www.3ds.com/careers/jobs/apprentice-web-development-536790

NPCI Exam Done ✅

Company Name: RagaAI

Roles:
1) SDE 1 (1-2 years of experience)
2) SDE Intern (2024 and 2025 grads)

📩How to Apply:
Please send your resume to my inbox or email me at gaurav007jha@gmail.com. Mention "SDE1 Application" or "SDE Intern Application" in your message.

Company Name: Mercor

Role: Backend Developer

Batch eligible: 2022 and 2023 passouts

Apply: https://www.linkedin.com/jobs/view/3787246529

Company Name: Eightfold

Role: Software Engineer

Batch eligible: 2022 and 2023 passouts

Apply: https://employee.eightfold.ai/careers/job/68738941874-software-engineer-frontend-fullstack--bangalore-karnataka-india?domain=eightfold.ai

Company Name: Siemens

Role: Software Engineer

Batch eligible: 2022 and 2023 grads

Apply: https://jobs.siemens.com/careers/job/563156117655600?microsite=siemens.com

Company Name: Sasken

Role: Associate Software Engineer

Batch eligible: 2022 and 2023 passouts

Apply: https://careers.sasken.com/job/ASSOCIATE-SOFTWARE-ENGINEER/21351444/

Coding Blocks is hiring for Mentor role in different domains.

Link to apply: https://docs.google.com/forms/d/e/1FAIpQLSctjaAtI31xJmWRwzNx4sm2npqWrCQiV_Ft2tovmcIaCZPRkQ/viewform

Eazotel startup is aggressively Hiring

Role: backend and frontend developers on an immediate basis.

No. of positions 20 to 30

Mail them your CV at hr@eazotel.com

Genoshi.io (a startup run by IIT Delhi undergrads) is hiring interns.

Backend Intern JD: https://docs.google.com/document/d/1DhR4RP0Bu-c4sAv5nwrQQjVLLvOBvOZOh6V7arsGMb4/edit?usp=sharing

Frontend Intern JD: https://docs.google.com/document/d/1qmy9OTJcD2NVZjYmyKtHUY4GxfZhwFO_zVG58xWTFxI/edit?usp=sharing

To apply, DM your resume to: https://www.linkedin.com/in/js-dhruv

The Interns are hiring for several roles.

If interested, apply: https://docs.google.com/forms/d/e/1FAIpQLSecoY3JuyLvpH_7yPVpiECyZ4T-pWNtFiZaZl__f1L8JRAR2w/viewform

Company Name: Wood

Role: Graduate Engineer Trainee

Batch eligible: 2022 and 2023 grads

Apply: https://ehif.fa.em2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/1343

Company Name: Cognizant

Role: Software Engineer

Batch eligible: 2023 grads only

Apply: https://app.joinsuperset.com/join/#/signup/student/jobprofiles/f207da4e-a7e1-48a0-b6ba-5f2fd6c719ca

P.S: Backup rakhlo even if you have a company to join, bcoz market is so volatile

function findShortestPath(N, bricks) {
  let visited = new Set();
  let queue = [[0, 0]]; 

  while (queue.length > 0) {
    let [position, moves] = queue.shift(); 

    if (position === N - 1) {
      return moves;
    }

    visited.add(position); 
    if (position + 1 < N && bricks[position] === bricks[position + 1] && !visited.has(position + 1)) {
      queue.push([position + 1, moves + 1]); 
    }

    if (position - 1 >= 0 && bricks[position] === bricks[position - 1] && !visited.has(position - 1)) {
      queue.push([position - 1, moves + 1]); 
    }

    for (let i = 0; i < N; i++) {
      if (bricks[i] === bricks[position] && !visited.has(i)) {
        queue.push([i, moves + 1]); 
      }
    }
  }
  return -1; 
}

Dorothy has been caught up

process.stdin.on('data', data => {
  inputArr.push(data.toString().trim());
  if (++count === 3) {
    console.log(divideMutantsIntoTeams(inputArr));
    process.exit(0);
  }
});

function divideMutantsIntoTeams(inputArr){
  let N = parseInt(inputArr[0], 10);
  let K = parseInt(inputArr[1], 10);
  let mutants = inputArr[2].split(' ').map(Number);


  if (N < K || new Set(mutants).size === 1 || N % K !== 0) {
    return -1;
  }


  mutants.sort((a, b) => a - b);
  let minNonCompatibilityScore = Infinity;

  for (let i = 0; i <= N - K; i++) {
    let diff = mutants[i + K - 1] - mutants[i];
    if (diff < minNonCompatibilityScore) {
      minNonCompatibilityScore = diff;
    }
  }

  return minNonCompatibilityScore;
}

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