Machine Learning vs. Deep Learning vs. AI Questions and Answers for Viva

Answer-1: Transfer learning is a technique in Deep Learning where a pre-trained model is fine-tuned on a new task, allowing the model to leverage existing knowledge and improve performance on smaller datasets.

Answer-2: In traditional programming, rules and logic are explicitly written by humans. In AI, systems learn patterns from data and improve their performance autonomously without explicit programming for each task.

Answer-3: An AI system typically consists of data, algorithms (machine learning models), a computational framework, and feedback mechanisms to improve performance over time.

Answer-4: Reinforcement learning is used in AI to train agents to make decisions in an environment by rewarding them for beneficial actions and penalizing them for harmful ones, such as in robotics and game playing.

Answer-5: Deep Learning improves image recognition by using CNNs to automatically extract and learn hierarchical features from images, improving accuracy and performance in tasks like object detection and facial recognition.

Answer-6: A neural network is the foundational structure of Deep Learning models. It consists of layers of interconnected neurons that process and transform data to identify patterns and make predictions.

Answer-7: Hyperparameters are the parameters that are set before training a model and control aspects like the learning rate, the number of layers in a neural network, or the number of trees in a random forest.

Answer-8: Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns and make decisions. Common examples include ReLU, Sigmoid, and Tanh functions.

Answer-9: Yes, Deep Learning techniques like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are commonly used for time-series analysis due to their ability to handle sequential data.

Answer-10: Machine Learning models automatically learn patterns from data, while traditional statistical methods often require predefined hypotheses or assumptions about the data before analysis.

Answer-11: Feature engineering involves selecting, modifying, or creating new features from raw data to improve the performance of Machine Learning models, especially in traditional Machine Learning approaches.

Answer-12: Deep Learning can handle unstructured data (e.g., images, audio), automatically learn features, and scale better with large datasets, whereas traditional ML requires manual feature extraction and works better with structured data.

Answer-13: A decision tree model splits data into subsets based on feature values to make decisions, creating a tree-like structure of nodes where each node represents a decision based on a feature.

Answer-14: Reinforcement learning is used in gaming AI to allow agents to learn strategies by interacting with the environment and receiving rewards or penalties, optimizing actions to maximize long-term success.

Answer-15: A support vector machine is a supervised learning model that finds the optimal hyperplane to separate different classes in the feature space, often used in classification tasks.

Answer-16: Regression is used to predict continuous numerical values, while classification is used to categorize data into discrete classes or labels.

Answer-17: Ensemble methods combine multiple models to improve performance. Examples include Random Forest, Boosting, and Bagging, where the output of several models is combined to make a final prediction.

Answer-18: Unsupervised learning is used to identify hidden patterns or groupings in data without labels, often used in clustering, dimensionality reduction, and anomaly detection.

Answer-19: A Generative Adversarial Network (GAN) consists of two neural networks (generator and discriminator) that compete with each other. The generator creates fake data, and the discriminator tries to distinguish it from real data, improving the generator?s output.

Answer-20: Deep Learning, especially CNNs, is used in autonomous vehicles for tasks like object detection, lane recognition, and decision-making based on sensor inputs such as cameras and LIDAR.

Answer-21: A Random Forest is an ensemble method that combines multiple decision trees to improve classification accuracy by averaging the results of different trees to reduce overfitting.

Answer-22: Feature scaling standardizes or normalizes data features to ensure that all features contribute equally to the model's learning process, improving the model's performance, especially in distance-based algorithms.

Answer-23: Model evaluation involves assessing the performance of a trained model using metrics like accuracy, precision, recall, F1-score, and confusion matrix, often through cross-validation or hold-out testing.

Answer-24: Imbalanced datasets can be handled using techniques like oversampling, undersampling, synthetic data generation (SMOTE), or adjusting class weights to ensure the model learns from all classes effectively.

Answer-25: AI is the broad field focused on creating intelligent systems. Machine Learning (ML) is a subset of AI that allows systems to learn from data. Deep Learning (DL) is a subset of ML that uses neural networks with multiple layers to model complex patterns in large datasets.

Answer-26: AI refers to the simulation of human intelligence in machines designed to think and learn like humans, including problem-solving, perception, and decision-making.

Answer-27: Machine Learning is a subset of AI that involves training models on data to make predictions or decisions without explicit programming. It includes supervised, unsupervised, and reinforcement learning.

Answer-28: Deep Learning is a subset of Machine Learning that uses artificial neural networks with many layers to analyze complex patterns in large datasets. It is particularly useful for tasks like image and speech recognition.

Answer-29: AI is the broad field encompassing intelligent systems. ML is a subset of AI that focuses on data-driven learning. DL is a specialized form of ML that uses deep neural networks for more complex tasks.

Answer-30: Machine Learning involves algorithms that learn from data using simpler models, while Deep Learning uses multi-layered neural networks to automatically learn hierarchical representations of data.

Answer-31: Machine Learning is more efficient when you have smaller datasets or less complex problems, while Deep Learning is suitable for large-scale datasets and tasks like image or speech recognition that require complex models.

Answer-32: Machine Learning can be classified into three main types: supervised learning, unsupervised learning, and reinforcement learning.

Answer-33: AI applications include natural language processing (NLP), image recognition, autonomous vehicles, recommendation systems, robotics, and predictive analytics.

Answer-34: Supervised learning is a type of Machine Learning where the model is trained on labeled data, with input-output pairs, to make predictions on unseen data.

Answer-35: Unsupervised learning involves training a model on unlabeled data to find hidden patterns or structures, such as clustering or dimensionality reduction.

Answer-36: Reinforcement learning is a type of Machine Learning where an agent learns by interacting with an environment and receiving rewards or penalties for actions, aiming to maximize cumulative reward.

Answer-37: A neural network in Deep Learning consists of layers of interconnected nodes (neurons). Each node processes data and passes the result to the next layer. Deep neural networks have multiple layers, enabling them to learn complex data representations.

Answer-38: A Convolutional Neural Network (CNN) is a type of deep neural network commonly used for image processing tasks. It uses convolutional layers to automatically learn spatial hierarchies in images.

Answer-39: A Recurrent Neural Network (RNN) is a type of neural network that is designed for sequence data, where outputs depend on previous inputs, making it useful for tasks like speech recognition and time-series forecasting.

Answer-40: Deep Learning can handle large volumes of unstructured data, automatically extract features, and learn complex patterns without manual feature engineering, making it more effective for tasks like image and speech recognition.

Answer-41: Deep Learning requires large datasets, significant computational resources, and long training times. It also lacks interpretability, which can make models difficult to understand or trust.

Answer-42: Data is essential in Machine Learning as it is used to train algorithms, allowing them to identify patterns, make predictions, and improve over time through learning from examples.

Answer-43: Deep Learning models use multiple layers of neural networks to automatically extract features and learn hierarchical data representations, whereas traditional ML models require manual feature extraction.

Answer-44: A loss function measures the difference between the model's predicted output and the actual output, guiding the optimization process during training to minimize errors.

Answer-45: Backpropagation is an algorithm used to train neural networks by calculating the gradient of the loss function and adjusting the weights of the network to minimize errors during training.

Answer-46: Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new, unseen data.

Answer-47: Overfitting can be avoided by using techniques like dropout, regularization, data augmentation, and cross-validation to ensure the model generalizes well to new data.

Answer-48: Underfitting happens when a model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test sets.

Answer-49: Machine Learning is used in NLP for tasks like text classification, language translation, sentiment analysis, and speech recognition by training models on text data to recognize patterns and make predictions.

Answer-50: Deep Learning, particularly CNNs, is used in computer vision tasks like object detection, image classification, and facial recognition by automatically learning features from images and analyzing them with multiple layers.