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BIOCUBE MATRICS Data Scientist Interview Questions, Process, and Tips
BIOCUBE MATRICS Data Scientist Interview Experiences
1 interview found
Anonymous
I applied via LinkedIn and was interviewed in Apr 2024. There was 1 interview round.
(5 Questions)
- Q1. What is Dropout & Batch Normalization?
- Ans.
Dropout is a regularization technique to prevent overfitting by randomly setting some neuron outputs to zero during training. Batch Normalization is a technique to normalize the inputs of each layer to improve training speed and stability.
Dropout randomly sets a fraction of neuron outputs to zero during training to prevent overfitting.
Batch Normalization normalizes the inputs of each layer to improve training speed and...
- Q2. Explain YOLO architecture, difference with SSD?
- Ans.
YOLO (You Only Look Once) is a real-time object detection system that processes images in a single pass, while SSD (Single Shot MultiBox Detector) is another object detection model that also aims for real-time processing but uses a different approach.
YOLO processes images in a single pass, making it faster than SSD which requires multiple passes.
SSD uses a fixed grid of boxes at different aspect ratios and scales to de...
- Q3. What is confusion Matrix?
- Ans.
Confusion Matrix is a table that is often used to describe the performance of a classification model.
It is a 2x2 matrix that summarizes the predictions of a classification model.
It shows the number of true positives, true negatives, false positives, and false negatives.
It is useful for evaluating the performance of a model by calculating metrics like accuracy, precision, recall, and F1 score.
- Q4. What are optimizers in Deep Learning Models?
- Ans.
Optimizers in Deep Learning Models are algorithms used to minimize the loss function by adjusting the weights of the neural network.
Optimizers help in updating the weights of the neural network during training to minimize the loss function.
Popular optimizers include Adam, SGD, RMSprop, and Adagrad.
Each optimizer has its own way of updating the weights based on gradients and learning rate.
Choosing the right optimizer ca...
- Q5. Difference between Right and Inner Join?
- Ans.
Right join includes all records from the right table and matching records from the left table, while inner join includes only matching records from both tables.
Right join keeps all records from the right table, even if there are no matches in the left table.
Inner join only includes records that have matching values in both tables.
Example: If we have a table of employees and a table of departments, a right join would in...
Interview Preparation Tips
- Computer Vision
- Deep Learning
- Python
- Machine Learning
- API
Skills evaluated in this interview
Data Scientist Jobs at BIOCUBE MATRICS
Interview questions from similar companies
I applied via Naukri.com and was interviewed in Jul 2021. There was 1 interview round.
Interview Questionnaire
1 Question
- Q1. Project Discussion… Question on Machine Learning algorithms, Stats, Sql
Interview Preparation Tips
Interview Questionnaire
1 Question
- Q1. First Round: Basic Statistics, Basic Python Programming, Past Projects. Second Round: Past Projects, Questions from computer vision, NLP, SQL, Basic Python.
I applied via Campus Placement and was interviewed before Sep 2020. There were 3 interview rounds.
Interview Questionnaire
1 Question
- Q1. Nothing much technical
Interview Preparation Tips
2. Fluency in English is important (depends on interview panel)
3. Clarity on what your talking about
I applied via Company Website and was interviewed before Jan 2020. There was 1 interview round.
Interview Questionnaire
1 Question
- Q1. Which job gives me do that work because of this job very important to me
Interview Preparation Tips
I applied via Recruitment Consultant and was interviewed in Sep 2020. There were 3 interview rounds.
Interview Questionnaire
1 Question
- Q1. Machine learning concepts[Regression and classification] ,Python and Sql Basics
Interview Preparation Tips
I applied via Referral and was interviewed in Dec 2021. There were 3 interview rounds.
(3 Questions)
- Q1. Explain your project?
- Q2. Machine learning algorithms
- Ans.
Machine learning algorithms are used to train models on data to make predictions or decisions.
Supervised learning algorithms include linear regression, decision trees, and neural networks.
Unsupervised learning algorithms include clustering and dimensionality reduction.
Reinforcement learning algorithms involve an agent learning through trial and error.
Examples of machine learning applications include image recognition, ...
- Q3. Model Evaluation technique
- Ans.
Model evaluation techniques are used to assess the performance of a machine learning model.
Common techniques include cross-validation, holdout validation, and bootstrap validation.
Metrics such as accuracy, precision, recall, and F1 score can be used to evaluate model performance.
Visualizations such as confusion matrices and ROC curves can also aid in model evaluation.
It is important to use multiple evaluation technique...
(1 Question)
- Q1. Deep learning concepts
Interview Preparation Tips
Be Honest
Be knowledgeable
Skills evaluated in this interview
HARSH VARDHAN Sharma
(1 Question)
- Q1. Questions related to machine learning
(1 Question)
- Q1. Question related to machine learning, projects and case study.
(1 Question)
- Q1. Salary discussion and position offered.
Interview Preparation Tips
(2 Questions)
- Q1. What is overfitting and underfitting
- Ans.
Overfitting occurs when a model learns the training data too well, leading to poor performance on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.
Overfitting: Model is too complex, fits noise in the training data, performs poorly on new data
Underfitting: Model is too simple, fails to capture underlying patterns in the data, performs poorly on both training and new...
- Q2. What are LLM Models
- Ans.
LLM models, or Language Model Models, are a type of machine learning model that focuses on predicting the next word in a sequence of words.
LLM models are commonly used in natural language processing tasks such as text generation, machine translation, and speech recognition.
They are trained on large amounts of text data to learn the relationships between words and predict the most likely next word in a given context.
Exa...
Interview Preparation Tips
Skills evaluated in this interview
I applied via Recruitment Consulltant and was interviewed in Aug 2023. There were 3 interview rounds.
(1 Question)
- Q1. About your project and experience
Core Python questions were asked
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BIOCUBE MATRICS Data Scientist Interview Process
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