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Basic question plus technical
Aptitude based questions
Typical SDE topics, duration 1 hour
I applied via LinkedIn and was interviewed in Jul 2023. There were 3 interview rounds.
In office. Easy 45 mins. Pen paper based.
CS fundamentals after aptitude. Pen paper based 30 mins.
To find the middle of a linked list, use two pointers - one moving at twice the speed of the other.
Initialize two pointers, slow and fast, both pointing to the head of the linked list.
Move the slow pointer by one node and the fast pointer by two nodes in each iteration.
When the fast pointer reaches the end of the list, the slow pointer will be at the middle node.
I applied via Naukri.com and was interviewed in Jun 2021. There was 1 interview round.
posted on 14 May 2022
I applied via Walk-in and was interviewed before May 2021. There were 3 interview rounds.
I applied via Naukri.com and was interviewed before Mar 2023. There were 2 interview rounds.
Basic coding questions (non-DSA) - Basic questions to reverse a string, check if string is palindrome, etc. If you have basic coding skills, you'll easily clear this round
I applied via LinkedIn and was interviewed in Nov 2024. There were 2 interview rounds.
Aptitude was quite easy with simple python questions
Asked basic questions on numpy and pandas
I applied via Campus Placement and was interviewed in Mar 2024. There was 1 interview round.
Hyperparameter tuning is the process of selecting the best set of hyperparameters for a machine learning model.
Hyperparameters are parameters that are set before the learning process begins.
Hyperparameter tuning involves adjusting hyperparameters to optimize the model's performance.
Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization.
Neural networks are trained using algorithms that adjust the weights and biases of the network based on the input data and desired output.
Neural networks are trained using a process called backpropagation, where the error between the predicted output and the actual output is used to adjust the weights and biases of the network.
Training data is fed into the neural network, and the network's output is compared to the des...
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It can be identified when a model performs well on training data but poorly on unseen data.
Techniques to prevent overfitting include cross-validation, regularization, and early ...
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