Boost your journey with 24/7 access to skilled experts, offering unmatched artificial intelligence homework help
Frequently Asked Questions
Q. 1) What are the differences between traditional computer vision methods and deep learning-based approaches?
.Traditional computer vision methods rely on handcrafted features, such as edge detection and image filters, using algorithms like SIFT and HOG. Deep learning-based approaches, on the other hand, use neural networks (e.g., convolutional neural networks, CNNs) to learn and identify features automatically from raw data, which makes them more flexible and accurate in tasks like image classification and object detection.
Q. 2) What are some real-world applications of natural language processing (NLP) in healthcare?
NLP in healthcare is used for tasks such as medical document analysis, electronic health records (EHR) management, patient monitoring, sentiment analysis for social media, and predictive analytics to assist in drug discovery, disease diagnosis, and personalized medicine.
Q. 3) What is the difference between supervised, unsupervised, and reinforcement learning in AI?
Supervised Learning involves training a model on labeled data, where the correct output is provided. Unsupervised learning deals with data that is not labeled, and the model discovers patterns or clusters within the data. Reinforcement learning is about training an agent to make decisions based on rewards and punishments in an environment without explicit training data; the goal is to learn actions that maximize a reward.
Q. 4) How do neural networks handle missing data during training?
.Neural networks typically do not handle missing data well because they require complete input data. Missing values can be imputed, the data can be preprocessed to remove rows with missing values, or strategies like dropout can be employed to ignore or randomly update missing values during training.
Q. 5) What are the ethical concerns associated with generative AI models like ChatGPT or DALL·E?
Ethical concerns include biases and fairness, misinformation, copyright infringement, privacy issues, and the potential for deep fakes, which can be harmful or misleading.
Q. 6) How can explainability be incorporated into complex AI models to make decisions transparent?
Explainability can be added using methods such as LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive Explanations), or through attention mechanisms in neural networks. These methods help in understanding why a model made a particular decision, making it more transparent and interpretable.
Q. 7) 2.9 Which of the following are hyperparameters? Batch Size Number of hidden layers Model Weights Activation Function.
Hyper-parameters: Batch Size, Number of hidden layers, and Activation Function. Not hyper-parameters: Model Weights.
Q. 8) 3.4) Which of the following statements is/are true? Learning using optimizers such as AdaGrad or RMSProp is adaptive, as the learning rate is changed based on a pre-defined schedule after every epoch. Since different dimensions have different impacts, adapting the learning rate per parameter could lead to a good convergence solution. Since the AdaGrad technique adapts the learning rate based on the gradients, it could converge faster but also suffers from an issue with the scaling of the learning rate, which impacts the learning process and could lead to a suboptimal solution. The RMSProp technique is similar to the AdaGrad technique, but it scales the learning rate using an exponentially decaying average of squared gradients. The Adam optimizer always converges at a better solution than the stochastic gradient descent optimizer.
Learning using AdaGrad or RMSProp is adaptive, as they change the learning rate based on gradients, helping the model converge faster but also presenting scaling challenges. RMSProp avoids some issues by using an exponentially decaying average, similar to AdaGrad. The Adam optimizer is usually a better choice because it combines the benefits of momentum and adaptive learning rates without as many issues.
Q. 9) Which of the following statements is/are true regarding Adam optimization? Adam combines the benefits of momentum optimization and RMSProp. Adam uses a fixed learning rate throughout the training process. Adam incorporates bias correction for the moving averages of gradients and squared gradients. Adam optimization is always guaranteed to converge faster than SGD with momentum.
Adam combines momentum optimization and RMSProp, uses adaptive learning rates, and corrects biases, which can help improve convergence compared to stochastic gradient descent (SGD). Adam does incorporate bias correction but might still be slower to converge compared to some optimization methods due to its use of a fixed learning rate throughout training.
Q. 10) Which of the following statements is/are true? Optimizers such as AdaGrad and RMSProp are adaptive because they adjust the learning rate dynamically after each epoch based on specific criteria. Adapting the learning rate per parameter can lead to better convergence since different dimensions have varying impacts on the optimization process. The AdaGrad technique adjusts the learning rate based on the gradients, which can lead to faster convergence but may encounter issues with scaling the learning rate, potentially resulting in suboptimal solutions. RMSProp is similar to AdaGrad but uses an exponentially decaying average of squared gradients to scale the learning rate.The Adam optimizer consistently converges to better solutions compared to the stochastic gradient descent optimizer.
Optimizers like AdaGrad and RMSProp adjust the learning rate based on gradients, making them adaptive, and can provide better convergence. AdaGrad and RMSProp use techniques to avoid scaling issues. Adam consistently provides a better solution compared to stochastic gradient descent, especially when combined with momentum optimization.
Popular Subjects for Artificial Intelligence
Boost your journey with 24/7 access to skilled experts, offering unmatched artificial intelligence homework help