Exploring Deep Learning Concepts

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

• Deep learning is a type of traditional machine learning.

• Deep learning requires more manual intervention than traditional machine learning.

• Deep learning uses deep neural networks to automatically learn features from data, while traditional machine learning relies on manual feature extraction and simpler models.

• Traditional machine learning uses deep neural networks for feature extraction.

Name a popular deep learning framework and describe its main features.

• PyTorch

• TensorFlow

• Keras

• Caffe

What is a neural network and what are its key components?

• Key components of a neural network include only input and output layers.

• A neural network is a type of biological brain structure.

• Neural networks are exclusively used for image processing tasks.

• A neural network is a computational model with key components: input layers, hidden layers, output layers, neurons, and weights.

Explain the concept of overfitting in deep learning and how it can be mitigated.

• Overfitting in deep learning is when a model performs well on training data but poorly on unseen data, and it can be mitigated through techniques like regularization, dropout, early stopping, and data augmentation.

• Overfitting occurs when a model is too simple and cannot learn from the training data.

• Overfitting is when a model performs equally well on both training and unseen data.

• Overfitting can be improved by increasing the learning rate and reducing the number of epochs.

What role does backpropagation play in training deep learning models?

• Backpropagation allows for efficient computation of gradients to update weights in deep learning models.

• Backpropagation is used to initialize weights in deep learning models.

• Backpropagation helps in selecting the best model architecture.

• Backpropagation is a technique for data augmentation in training.