Question 1

?What distinguishes deep neural networks from traditional machine learning models

Accepted Answer

Deep neural networks consist of multiple layers of interconnected neurons, enabling them to automatically learn hierarchical representations of data. Unlike traditional machine learning models, deep neural networks can handle large, complex datasets and extract intricate patterns and features, making them highly effective for tasks like image and speech recognition.

Question 2

?What are some common challenges faced when training deep neural networks

Accepted Answer

Training deep neural networks can be challenging due to issues such as vanishing gradients, overfitting, and computational complexity. Vanishing gradients occur when gradients become extremely small during backpropagation, hindering learning in deeper layers. Overfitting can occur when the model learns to memorize training data instead of generalizing patterns, leading to poor performance on unseen data. Additionally, training deep neural networks often requires substantial computational resources, making scalability a concern.

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