Akamai Technologies
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I applied via Approached by Company and was interviewed in Nov 2024. There were 3 interview rounds.
posted on 27 Aug 2024
I applied via Referral and was interviewed in Jul 2024. There were 3 interview rounds.
I chose Infor because of its reputation for innovative technology solutions and its commitment to employee development.
Infor is known for its cutting-edge technology solutions in the industry.
I was impressed by Infor's focus on employee growth and development opportunities.
I believe Infor's values align with my own professional goals and aspirations.
I thrive under pressure by staying organized, prioritizing tasks, and maintaining a positive attitude.
I stay organized by creating to-do lists and breaking down tasks into manageable steps.
I prioritize tasks based on deadlines and importance to ensure that critical work is completed first.
I maintain a positive attitude by taking short breaks to recharge, practicing deep breathing exercises, and seeking support from col
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 Referral and was interviewed in Dec 2023. There was 1 interview round.
I used the Random Forest algorithm in my project.
Random Forest is an ensemble learning method that combines multiple decision trees to make predictions.
It is used for both classification and regression tasks.
Random Forest reduces overfitting and provides feature importance.
Example: I used Random Forest to predict customer churn in a telecom company.
SQL is a programming language used for managing and manipulating relational databases. A database is a structured collection of data.
SQL is used to retrieve, insert, update, and delete data from a database.
A database is a software system that stores and organizes data in a structured manner.
SQL allows users to define the structure of a database, create tables, and establish relationships between tables.
Examples of data
Joins in SQL are used to combine rows from two or more tables based on a related column between them.
Joins are used to retrieve data from multiple tables in a single query.
Common types of joins include inner join, left join, right join, and full outer join.
Joins are performed using the JOIN keyword and specifying the columns to join on.
Joins can be used to combine tables based on matching values or non-matching values.
...
I applied via Company Website and was interviewed in Dec 2023. There were 3 interview rounds.
Standard question from sql and python in hackerrank
Reverse a linked list by changing the direction of pointers
Start with three pointers: current, previous, and next
Iterate through the linked list, updating pointers to reverse the direction
Return the new head of the reversed linked list
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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