Deep Learning Interview Questions and Answers


Q. What is deep learning, and how does it differ from traditional machine learning?

Ans. Deep learning is a type of machine learning that utilizes artificial neural networks with multiple layers to process large amounts of data and make decisions/predictions. It differs from traditional machine learning as it can learn and make decisions on its own, rather than relying on pre-defined rules or algorithms.



Q. What are some commonly used applications of deep learning?

Ans. Common applications of deep learning include image and speech recognition, natural language processing, and predictive modeling. It used in many domains like e-commerce, healthcare, technology, finance, and the auto industry.



Q. How does a neural network learn?

Ans. A neural network learns through a process called backpropagation, in which it compares its output to the desired result and adjusts its weights and biases accordingly. This process repeated until the network can predict accurately as per your requirements.



Q. What is overfitting, and how can it be avoided in deep learning models?

Ans. Overfitting occurs when a model is trained too specifically to a particular dataset, leading it to perform poorly on new or unseen data. To avoid overfitting, it is important to use a large and diverse training dataset and to regularly test the model's performance on validation and test data.



Q. What is the difference between supervised and unsupervised learning in deep learning?

Ans. Supervised learning involves training a model on labeled data, where the correct output provided for each input. Unsupervised learning involves training a model on unlabeled data. Unsupervised learning allows the model to discover patterns and relationships on its own.