Prepfully logo
  • Browse Coaches
  • Login
BetaTry Out Our New AI Mock Interviewer – Your Smartest Way to Ace Any Interview!Try Our AI Mock Interviewer
Try Now
NewRegister as a coach and get a $100 bonus on your first completed session if you're on the Prepfully Request for Coaches list.Coach $100 Bonus
Read More
LimitedSummer Deal: Heavy discounts on all Prepfully sessions.Summer Deal: Discounts
Book Now

Your AI Wingman for your next interview

The most comprehensive bank Interview Answer Review tooling available online.

Cutting-edge AI technology meets personalized feedback. Improve your interview answers with insightful guidance provided by a model trained against more than a million human-labelled interview answers.
  • Company rubrics
  • Role-level optimisations
  • Trained on 1mil+ answers
System Design
3 years ago
Design a parking lot management system.
Machine Learning Engineer

Intel

Get answer reviewed by AI
3 years ago
ML Knowledge
3 years ago
Can you break down the concept and workflow of a Neural Network?
Machine Learning EngineerData Scientist

Intel

SeatGeek

Productboard

A neural network is a function approximation algorithm inspired by the brain. It is a dense network of nodes, which store values, and edges, which connect nodes. The simplest model is a dense sequential network which has layers of nodes. This network would be called dense when all nodes of one layer are connected to the nodes of the next layer. The network is called sequential because each layer activates one after the other. 


The value of a node is determined by all of the nodes that are connected to it in the previous layers. Each of these values will be multiplied by a weight, and then a bias will be added. This value is passed through an activation function and then result is then the value of the node. 


Typical activation functions are sigmoid, which is shaped like a very wide S, and a relu (rectified linear unit), which is like a hockey stick. The sigmoid activation function is tied to the way that axons in the brain have threshold response functions. 


Neural networks are trained by using the gradient between the output value in the dataset and predicted value. This update is propagated through the network using backpropagation. 


Neural networks are useful because they are universal approximators. This means that an infinitely wide network with one layer input input and output can represent an arbitrary function. This contributes to them being adept at so many tasks.

Get answer reviewed by AI
3 years ago
ML Knowledge
3 years ago
How does an LSTM's architecture and capabilities compare to a traditional RNN?
Machine Learning EngineerData Scientist

Intel

Springboard

Instagram Logo

Instagram

Get answer reviewed by AI
3 years ago
ML Knowledge
3 years ago
What is your proficiency with ML tools such as TensorFlow or PyTorch?
Machine Learning Engineer

Intel

LogMeIn Logo

LogMeIn

NetEase Logo

NetEase

Get answer reviewed by AI
3 years ago
ML Knowledge
3 years ago
What are some of the loss functions you have used or are aware of?
Machine Learning Engineer

Intel

Databricks Logo

Databricks

Chewy Logo

Chewy

Get answer reviewed by AI
3 years ago
ML Knowledge
3 years ago
How would you outline the backpropagation algorithm in the context of neural networks?
Machine Learning Engineer

Intel

Flexport Logo

Flexport

MongoDB Logo

MongoDB

backpropagation is a step in estimating the parameters of a model numerically using an optimization algorithm such as gradient decsent or adam. The optimization algorithm involves applying chain rule to the model,  it then makes a forward pass to calculate the gradient of the data for the calculated gradient, and then during the backpropagation, the optimizer updates the parameters of a model using the calculated gradient, a learning rate and the specific formulation of the optimizer. For Stochasting gradient decsent the delta is simply the learning rate multiplied by the gradient. 

Get answer reviewed by AI
3 years ago
System Design
3 years ago
Create a system to track user geolocations in real-time.
Machine Learning Engineer

Intel

Get answer reviewed by AI
3 years ago
Coding
3 years ago
Can you identify the blanagrams from the words given to you - cinema and iceman?
Machine Learning Engineer

Intel

Tinder Logo

Tinder

Snowflake Logo

Snowflake

Get answer reviewed by AI
3 years ago
System Design
3 years ago
What are the key considerations when designing a highly available and secure distributed backup solution?
Machine Learning Engineer

Intel

Get answer reviewed by AI
3 years ago
ML Knowledge
3 years ago
What is the Random Forest concept, and how does it apply to machine learning?
Machine Learning Engineer

Intel

ASML Logo

ASML

TripAdvisor Logo

TripAdvisor

The random forest algorithm is an ensemble method that combines multiple decision trees to make a prediction. It is very flexible and can be applied to a large number of tasks. It functions by creating a random decision tree and fitting it to the data, typically with a subset of features available. The final prediction is the average (or majority rule) decision. 


This is like having a number of interviews evaluating a candidate. Each one will be interested in a different subset of qualifications, and by averaging them, all the view points are incorporated. 

Get answer reviewed by AI
3 years ago

Try Free AI Interview

Question of the week

We'll send you a weekly question to practice for:

Showing 71 to 80 of 104 results

Previous678910Next

*All interview questions are submitted by recent Intel Machine Learning Engineer candidates, labelled and categorized by Prepfully, and then published after being verified by Machine Learning Engineers at Intel.

  • Meta Senior vs Staff Engineer Interview Expectations
  • Atlassian Software Engineer Interview
  • Cash App Machine Learning Engineer Interview
  • Stripe Machine Learning Engineer Interview
  • Canva Backend Engineer Interview
  • Google Technical Program Manager Interview
  • Company
  • FAQs
  • Contact Us
  • Become An Expert
  • Services
  • Practice Interviews
  • Interview Guides
  • Interview Questions
  • Watch Recorded Interviews
  • Gift sessions
  • AI Interview
  • Social
  • Twitter
  • Facebook
  • LinkedIn
  • YouTube
  • Legal
  • Terms & Conditions
  • Privacy Policy
  • Illustrations by Storyset

© 2025 Prepfully. All rights reserved.

Prepfully logo

Please log in to view more questions.

Not a member yet? Sign up for free.