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.

Define Neuron. Explain McCulloch-Pitts Neural Model in detail.

Explain what is meant by deep learning and describe the different types of deep learning.

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

Explain the difference between Machine Learning and Deep Learning.

Write a short note on neural network.

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

Explain the significance of depth in deep neural networks and how increasing the number of layers affects model performance.

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

Explain the process of initializing weights in a neural network. Why is proper initialization important?

Compare batch gradient descent and stochastic gradient descent.

Explain different types of Autoencoders.

What is the objective of the empirical risk minimization? Explain the principle of Empirical Risk Minimization.

Explain the importance of the gradient descent algorithm in deep learning.

What is Multilayer Perceptron? Explain Backpropagation algorithm in detail.

Explain the convolution operation in CNNs, its significance, and how it captures spatial features / why CNNs are used in deep learning

Explain any two short notes on advanced deep learning topics including Structured Output in Convolutional Networks and AlexNet

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

Write the difference between Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)

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

Explain the role of filters or kernels in CNNs

Briefly discuss convolutional neural networks in deep learning.

Explain the variants of the basic convolution function in detail.

What is Long Short Term Memory in Neural Network? Explain.

Explain the difference between RNN and Bidirectional RNN.

What are Bidirectional RNNs? Explain in detail.

Define Recurrent Network? Discuss about Deep Recurrent Networks.

Write about Gated RNNs.

How are deep generative models used in machine learning, and what are some of their key applications?

Give an overview of MCMC sampling.

What is the difference between Autoencoders and RBM? Discuss.

Give an overview of the following:

Write short notes on any two of: Deep Generic Models, Gibbs Sampling, Feed Forward Network, Artificial Intelligence vs Deep Learning.

What is difference between artificial intelligence, machine learning and deep learning.

Discuss hybrid models that combine the benefits of VAEs and GANs. How do VAE-GAN hybrid models work?

Write the difference between Convolutional Neural Network and Recurrent Neural Network.

Explain the difference between Machine Learning and Artificial Intelligence.

Discuss the role of Deep Learning in Data Science.