Engineering Course Materials
Recurrent Neural Networks (RNNs) are deep learning models designed for sequential data, where each output depends on previous inputs. They
Continue readingCASE STUDY – DEEP LEARNING – UNIT 3 RECURRENT NEURAL NETWORKS
Convolutional Neural Networks (CNNs) are a type of deep learning model designed to process and analyze visual data. They use
Continue readingCASE STUDY – DEEP LEARNING – UNIT 2 CONVOLUTIONAL NEURAL NETWORKS
Deep networks are artificial neural networks with multiple hidden layers that allow them to learn complex patterns and representations from
Continue readingCASE STUDY – DEEP LEARNING – UNIT 1 DEEP NETWORKS BASICS
Model Evaluation is the process of assessing how well a machine learning model performs on unseen data. It uses metrics
Continue readingUNIT IV – MODEL EVALUATION -16 MARKS WITH ANSWERS
Deep Networks are neural networks with multiple hidden layers that learn hierarchical features from data. They enable complex pattern recognition
Continue readingUNIT 1 – DEEP NETWORKS – 16 MARKS WITH ANSWERS
Deep networks are neural networks with multiple hidden layers that learn complex patterns from large data. They are the foundation
Deep networks are neural networks with multiple hidden layers that enable learning complex patterns from large datasets. They form the
Continue readingUNIT 1 – DEEP NETWORKS BASCIS-2 MARKS WITH ANSWERS
Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data by maintaining a memory of
Continue readingUNIT 3–RECURRENT NEURAL NETWORKS – 2 MARKS WITH ANSWERS
Convolutional Neural Networks (CNNs) are deep learning models designed to process grid-like data such as images by applying convolutional filters
Continue readingUNIT 2 – CONVOLUTIONAL NEURAL NETWORKS – 2 MARKS WITH ANSWERS
Model evaluation is the process of assessing how well a machine learning or deep learning model performs on unseen
Continue readingUNIT 4 – MODEL EVALUATION -2 MARKS WITH ANSWERS