How would you pull data from their database - no info was given about their db?
Lead Data Scientist Interview Questions
347 lead data scientist interview questions shared by candidates
Questions on K means clustering, Decision Trees, Random Forests, Neural Networks, and many other ML and Data Mining concepts.
Resume walkthrough SQL, Python live coding Explain stats concepts to people from both technical and non-technical background Basic ML concept understanding questions Case study
given a integer we need to find the digit which is next biggest using comprising all the digit of integer
tell me about yourself. Telecom background.
why catboost and xgboost is better than gradient boosting in sklearn. How GradientBoosting is working.
The business case with a data set of 30k of observation with inbalanced data, with a long list of items to do, one of them being to run a "deep learning" model. When I tried to argue which data would make this model overfit and there it would not make sense, the "Data Scientist" replied, that is only for benchmark. They dont respect the candidate time, asking a lot of non-sense, and do not have knowledge to debate about the theory on how that would work or not
My background, my experience, what I would do in certain management situations
1. Fundamental Concepts Explain the difference between image segmentation and object detection. How would you choose one over the other in a project? Can you describe the role of convolutional layers in a Convolutional Neural Network (CNN) and how they differ from fully connected layers? Discuss the concept of image feature extraction. What are some common methods used for this, and how do you decide which to use? 2. Advanced Algorithms and Techniques How do you approach the problem of detecting objects at different scales within an image? Discuss the use of techniques like Feature Pyramid Networks (FPN) or Multi-Scale Context Aggregation. Explain the concept and implementation of the Region-Based Convolutional Neural Network (R-CNN) and its variants (Fast R-CNN, Faster R-CNN, Mask R-CNN). How do they improve upon each other? Describe the process and benefits of using Generative Adversarial Networks (GANs) for image synthesis and augmentation. 3. Practical Applications and Challenges How would you handle image data from different sources with varying resolutions, lighting conditions, and perspectives in a unified model? Discuss the challenges and solutions in training deep learning models for computer vision tasks when dealing with limited labeled data. Can you explain the concept of Transfer Learning and how it can be applied to improve performance on image classification tasks with limited data? 4. Performance and Optimization What are some techniques to optimize the performance of computer vision models in real-time applications? Discuss trade-offs between accuracy and speed. How do you evaluate the performance of an image processing model? What metrics do you use and why? Discuss methods for dealing with overfitting in deep learning models, especially in the context of image classification and object detection. 5. Cutting-Edge Technologies and Research What are the latest advancements in self-supervised learning for computer vision, and how do they compare with traditional supervised methods? Discuss the impact of Transformer models in computer vision tasks. How do they differ from CNNs in processing image data? Explain how you would approach integrating computer vision systems with other modalities, such as text or audio, for a multimodal application. 6. Real-World Scenarios You are given a dataset of images with various occlusions and distortions. How would you preprocess and augment the data to improve model robustness? How would you approach designing a system for real-time facial recognition in a security application? What considerations would you have for accuracy, speed, and privacy? Describe a situation where you had to debug and improve a computer vision model that was underperforming. What steps did you take to diagnose and resolve the issues?
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