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I applied via Company Website and was interviewed before May 2023. There were 3 interview rounds.
GD round where check your communication skills
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Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.
Adam optimizer uses adaptive learning rates for each parameter.
Gradient Descent optimizer has a fixed learning rate for all parameters.
Adam optimizer includes momentum to speed up convergence.
Gradient Descent optimizer updates parameters b...
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.
Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.
Consider using Leaky ReLU or Sigmoid for output layers depending on the task.
ReLU is computationally efficient and helps in preventing the vanishing gradient prob...
I applied via Naukri.com and was interviewed in Jan 2024. There was 1 interview round.
I applied via Referral and was interviewed before May 2023. There were 4 interview rounds.
180 mins of online test with camera ON. Major topics include Excel, Aptitude, Python, Statistics and Case Study
Apriori method is a popular algorithm for frequent itemset mining in data mining.
Used for finding frequent itemsets in transactional databases
Based on the concept of association rule mining
Involves generating candidate itemsets and pruning based on support threshold
Example: If {milk, bread} is a frequent itemset, then {milk} and {bread} are also frequent
Train-test split is a method used to divide a dataset into training and testing sets for model evaluation in Scikit learn.
Split the dataset into two subsets: training set and testing set
Training set is used to train the model, while testing set is used to evaluate the model's performance
Common split ratios are 70-30 or 80-20 for training and testing sets
Example: X_train, X_test, y_train, y_test = train_test_split(X, y,
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