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LLM stands for Language Model. Fine tuning involves adjusting hyperparameters and training on specific data.
LLM stands for Language Model, which is a type of AI model that predicts the next word in a sentence.
Fine tuning LLM involves adjusting hyperparameters such as learning rate, batch size, and number of training epochs.
Fine tuning also involves training the LLM on specific data related to the task at hand, such as ...
RAG stands for Red, Amber, Green and is a project management tool used to visually indicate the status of tasks or projects.
RAG is commonly used in project management to quickly communicate the status of tasks or projects.
Red typically indicates tasks or projects that are behind schedule or at risk, Amber indicates tasks that are on track but may need attention, and Green indicates tasks that are on schedule or complet...
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Code for parsing a triangle
Use a loop to iterate through each line of the triangle
Split each line into an array of numbers
Store the parsed numbers in a 2D array or a list of lists
The ASCII value is a numerical representation of a character. It includes both capital and small alphabets.
ASCII values range from 65 to 90 for capital letters A to Z.
ASCII values range from 97 to 122 for small letters a to z.
For example, the ASCII value of 'A' is 65 and the ASCII value of 'a' is 97.
My friends think of me as reliable, supportive, and always up for a good time.
Reliable - always there when they need help or support
Supportive - willing to listen and offer advice
Fun-loving - enjoys socializing and trying new things
General aptitude basics
Mcq and basic ml model building
I applied via Approached by Company
Transformers are a type of neural network architecture that utilizes self-attention mechanisms to process sequential data.
Transformers use self-attention mechanisms to weigh the importance of different input elements, allowing for parallel processing of sequences.
Unlike RNNs and LSTMs, Transformers do not rely on sequential processing, making them more efficient for long-range dependencies.
Transformers have been shown ...
Different types of Attention include self-attention, global attention, and local attention.
Self-attention focuses on relationships within the input sequence itself.
Global attention considers the entire input sequence when making predictions.
Local attention only attends to a subset of the input sequence at a time.
Examples include Transformer's self-attention mechanism, Bahdanau attention, and Luong attention.
GPT is a generative model while BERT is a transformer model for natural language processing.
GPT is a generative model that predicts the next word in a sentence based on previous words.
BERT is a transformer model that considers the context of a word by looking at the entire sentence.
GPT is unidirectional, while BERT is bidirectional.
GPT is better for text generation tasks, while BERT is better for understanding the cont
Data scientists analyze data to gain insights, machine learning (ML) involves algorithms that improve automatically through experience, and artificial intelligence (AI) refers to machines mimicking human cognitive functions.
Data scientists analyze large amounts of data to uncover patterns and insights.
Machine learning involves developing algorithms that improve automatically through experience.
Artificial intelligence r...
I applied via Naukri.com and was interviewed in Sep 2021. There were 4 interview rounds.
I applied via Naukri.com and was interviewed before Oct 2022. There were 3 interview rounds.
Easy to solve if you have basic mathematics understanding.
They will test your presence of mind and thinking.
I applied via Campus Placement and was interviewed in Aug 2024. There were 2 interview rounds.
Aptitude test consists of 40 questions.
I am a data scientist with experience in developing predictive models and analyzing large datasets.
Developed a predictive model for customer churn prediction using machine learning algorithms
Analyzed sales data to identify key trends and patterns for business optimization
Implemented natural language processing techniques for sentiment analysis of customer reviews
Develop a predictive model to identify potential customers for a new product launch.
Define the target variable and features to be used in the model
Collect and preprocess relevant data for training the model
Select an appropriate machine learning algorithm and train the model
Evaluate the model's performance using metrics like accuracy, precision, and recall
Use the model to predict potential customers for the new product
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