Deep Learning (AD-601) - Important Questions

Compare biological neural networks with artificial neural networks and explain the limitations of current deep learning models compared to the human brain.

What is deep learning? Explain the structure and units of a biological neuron.

Explain single-layer perceptron and multilayer perceptron (MLP) in detail, and explain how MLP overcomes the limitations of single-layer perceptrons.

Discuss different optimization techniques for training feedforward networks, including gradient descent, stochastic gradient descent and Adam optimizer.

Explain different types of autoencoders in detail.

Explain the convolution operation in CNNs, its significance, and how it captures spatial features from images.

Write short notes on structured outputs in convolutional networks and AlexNet architecture.

Explain what LeNet is and discuss its role and usage in convolutional networks and object recognition.

Explain the role of pooling layers in CNNs, including max pooling and average pooling.

Explain the role of filters or kernels in CNNs.

Explain the variants of the basic convolution function in detail.

What is Long Short Term Memory in neural network? Explain.

What are bidirectional RNNs? Explain in detail with architecture and advantages over unidirectional RNNs.

Give an overview of MCMC sampling and explain Gibbs sampling with its use in deep generative models.

What is the difference between autoencoders and RBM? Discuss.