In which of the following real-life scenarios is deep learning commonly applied? Question 1 options: Managing traffic flow in urban environments using smart city technology. Detecting fraudulent transactions in banking systems. Predicting the outcome of sports matches based on player statistics. Translating ancient manuscripts into modern languages.

Which of the following options is not a reason for traditional machine learning algorithms to promote the development of deep learning?Dimensional disasterLocal invariance and smooth regularizationManifold learningFeature Engineering

Think of the future, what might we use machine learning for in the future?

Deep learning is a subset of machine learning algorithms that uses multiple layers to progressively extract information from the raw input to give better output.Select one:a. Trueb. False

My interest in the Vision and AI lab is driven by its focus on cutting-edge research in computer vision and artificial intelligence. The lab's projects encompass a wide range of exciting topics, including long-tailed learning, adversarial robustness of deep neural networks, and multi-modal neural encodings. These areas align perfectly with my interests, especially my past experience in traffic sign detection using deep learning. I'm eager to contribute to projects that advance the field of computer vision while addressing challenges such as long-tailed data distribution and adversarial attacks.

Q.8 Real-Time decisions, Game AI, Learning Tasks, Skill acquisition, and Robot Navigation are applications of .............?1. Unsupervised Learning: Regression2. None of the above3. Reinforcement Learning4. Supervised Learning: Classification