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Cuelogic
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I applied via Recruitment Consultant and was interviewed in Mar 2021. There were 5 interview rounds.
I applied via Recruitment Consultant and was interviewed in Dec 2020. There was 1 interview round.
Custom word embedding models can be trained using algorithms like Word2Vec and GloVe.
Choose a corpus of text data to train the model on
Preprocess the text data by tokenizing and cleaning it
Use an algorithm like Word2Vec or GloVe to train the model
Tune hyperparameters like vector size and window size for optimal results
Evaluate the model using metrics like cosine similarity and word analogy tests
Text clustering algorithms group similar documents together based on their content.
K-means clustering
Hierarchical clustering
DBSCAN
Latent Dirichlet Allocation (LDA)
Non-negative Matrix Factorization (NMF)
Agglomerative clustering
Spectral clustering
I performed standard text pre-processing techniques such as tokenization, stop word removal, and stemming.
Tokenization to split text into individual words or phrases
Stop word removal to eliminate common words that do not add meaning
Stemming to reduce words to their root form
Lowercasing to standardize text
Removing punctuation and special characters
Handling misspellings and abbreviations
I applied via Campus Placement and was interviewed in Nov 2020. There were 4 interview rounds.
Cuelogic interview questions for popular designations
I applied via Naukri.com and was interviewed in Jul 2020. There were 6 interview rounds.
I applied via Naukri.com and was interviewed in Oct 2020. There was 1 interview round.
I applied via Company Website and was interviewed in Mar 2020. There were 4 interview rounds.
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I applied via Company Website and was interviewed before Dec 2020. There were 4 interview rounds.
I applied via Referral and was interviewed before Jan 2021. There was 1 interview round.
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