5.b) Explain the challenges that occur in neural network optimization in detail. Answer: Challenges in Neural Network Optimization Convex and non-convex functions are important concepts in machine learning, particularly in…
3 B] What is regularization? How does regularization help in reducing overfitting? What is Regularization? Regularization is a technique in machine learning used to prevent overfitting by introducing additional constraints…
3 a] Explain the working of deep forward networks. A Deep Forward Network (DFN), also known as a deep neural network, is a type of artificial neural network that maps…
5.a) Explain empirical risk minimization. Answer: Empirical risk minimization
Write a note on Speech Recognition and NLP Answer:- Speech Recognition: Natural Language Processing (NLP):
Explain the LSTM working principle along with equations Answer:- Working Principle of LSTM The Long Short-Term Memory (LSTM) network is designed to learn long-term dependencies in sequential data by overcoming…
Discuss bidirectional RNNs Answer:- Bidirectional Recurrent Neural Networks (BiRNNs)
Explain how the recurrent neural network (RNN) processes data sequences Answer:- Recurrent Neural Network (RNN) Data sequences :- This graph visually demonstrates the recurrent structure and flow of data within…
Write a note on support vector machines and PCA Answer:- Support Vector Machines (SVMs): Support Vector Machines are supervised learning algorithms primarily used for classification and regression tasks. SVMs work…
Explain in detail about the supervised learning approach by taking a suitable example Answer:- Supervised learning algorithms learn to map input data (x) to an output (y) using labeled examples…
