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LSTM addresses vanishing gradient problem in RNN and captures long-term dependencies better than GRU.
LSTM has three gates (input, output, forget) which help in preserving long-term dependencies.
GRU has two gates (reset, update) which are simpler but may not capture long-term dependencies as effectively as LSTM.
LSTM is better at avoiding vanishing gradient problem compared to RNN due to its gating mechanism.
LSTM is more
Build a simple cat and dog image classifier
Case study design oka6y
Approach involves data preprocessing, model training, evaluation, and interpretation.
Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.
Split the data into training and testing sets.
Train the logistic regression model on the training data.
Evaluate the model using metrics like accuracy, precision, recall, and F1 score.
Interpret the model coefficients to under...
I would seek opportunities to apply my skills in related fields within the company.
Explore other departments or teams within the company that may have projects related to my field of interest
Offer to collaborate with colleagues in different departments to bring a new perspective to their projects
Seek out professional development opportunities to expand my skills and knowledge in related areas
Questions related to basic coding were asked, and some background on projects and discussions alongside maths and statistics concepts
Simple mediam DSA style questions around ML
I applied via Company Website and was interviewed in Dec 2024. There was 1 interview round.
Easy topics arrays, sequence sum.
I applied via Walk-in and was interviewed in Dec 2020. There was 1 interview round.
based on 2 interviews
Interview experience
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