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Coding
2 years ago
Could you outline an algorithm for decoding strings with hierarchical nested parentheses?
Machine Learning Engineer

Microsoft

Mapbox

LiveRamp

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2 years ago
ML Knowledge
2 years ago
Can you elucidate the principle of a Random Forest in machine learning?
Machine Learning Engineer

Microsoft

ASML

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. 

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2 years ago
ML Knowledge
2 years ago
Could you explain the concept of a perceptron?
Machine Learning Engineer

Microsoft

Peloton Logo

Peloton

SAP Concur Logo

SAP Concur

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2 years ago
ML Knowledge
2 years ago
Can you elaborate on what an attention model entails?
Machine Learning Engineer

Microsoft

Calm Logo

Calm

Infineon Logo

Infineon

An attention model is an example of a hyper network where the weights of model are determined by the input itself. In the attention mechanism, this occurs in that each token of the input sequence is compared with all others in the context window to determine the next token. The Attention mechanism by default does not care about the order of the input, which is ironic because of the success it has found in next token prediction. This is the basis for the LLMs. The prompt (which may be modified on the backend) will be used and then the next token will be predicted for the answer, and then the answer is built up token by token. 

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2 years ago
ML Knowledge
2 years ago
If you were faced with a high-dimensional dataset in a machine learning project, how would you handle it?
Machine Learning Engineer

Microsoft

Marqeta Logo

Marqeta

Airtable Logo

Airtable

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2 years ago
ML Knowledge
2 years ago
Could you define the concepts of overfitting and underfitting in machine learning, and explain their relevance in model development?
Machine Learning Engineer

Microsoft

HubSpot Logo

HubSpot

AppDynamics Logo

AppDynamics

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2 years ago
ML Knowledge
2 years ago
What separates LSTMs from RNNs in terms of memory and sequential data processing?
Machine Learning EngineerData Scientist

Microsoft

Springboard Logo

Springboard

Instagram Logo

Instagram

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2 years ago
ML Knowledge
2 years ago
Explain how the bias-variance tradeoff functions in machine learning, and can you describe a specific scenario where this tradeoff has had a significant effect on a model's performance?
Machine Learning Engineer

Microsoft

Palo Alto Networks Logo

Palo Alto Networks

GoCardless Logo

GoCardless

bias is the average error of the model and variance is the variance of the model prediction. A model with high bias and low variance have predictions mapped into the same place in the space, a model with high variance and low bias has scattered predictions. The first one suggests that the model is too simple, it has high training error and testing error, the second one suggests overfitting when the model has low training error but high test error. I have seen this happen when a deep learning model starts overfitting, in that case I have used techniques to mitigate it like early stopping, regularization and dropout layer. 

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2 years ago
System Design
2 years ago
Design a management system for database clusters at scale.
Machine Learning Engineer

Microsoft

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2 years ago
ML Knowledge
2 years ago
How does the size of the input data affect the time complexity of training an SVM? How is the time complexity for SVM inference calculated? Can you explain the effect of different kernels on the time complexity of SVMs?
Machine Learning Engineer

Microsoft

Mayo Clinic Logo

Mayo Clinic

ASML

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2 years ago

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