Deep Learning
Comprehensive course covering mathematical foundations of Artificial Neural Networks, CNNs for computer vision, RNNs/LSTMs for sequential modeling, optimization techniques, and modern deep learning platforms (H2O.ai, Dato GraphLab, Theano, Caffe).
4
Credits
4 Hrs/Week
Teaching Scheme
5
Syllabus Units
📂 Syllabus Units & Study Modules
5 Units Total
Unit I • 8 Hrs
Unit I - Introduction to Deep Learning
Syllabus Topics Covered:
• 1.1 What is Deep Learning?
• 1.2 Why Deep Learning?
• 1.3 What is a neural network?
• 1.4 Neural networks - Neurons, layers, weights, and biases
• +2 more topics...
• 1.2 Why Deep Learning?
• 1.3 What is a neural network?
• 1.4 Neural networks - Neurons, layers, weights, and biases
• +2 more topics...
Unit II • 14 Hrs
Unit II - Artificial Neural Network (ANN)
Syllabus Topics Covered:
• 2.1 Artificial Neural Network (ANN)
• 2.1.1 The architecture of an artificial neural network
• 2.2.2 Single layer perceptron
• 2.1.3 Multilayer perceptron
• +8 more topics...
• 2.1.1 The architecture of an artificial neural network
• 2.2.2 Single layer perceptron
• 2.1.3 Multilayer perceptron
• +8 more topics...
Unit III • 14 Hrs
Unit III - Convolutional Neural Networks
Syllabus Topics Covered:
• 3.1 Introduction to CNNs
• 3.2 Convolution and pooling layers
• 3.3 CNN Architectures (LeNet, AlexNet, VGGNet, ResNet)
• 3.4 Working of CNN layers
• +4 more topics...
• 3.2 Convolution and pooling layers
• 3.3 CNN Architectures (LeNet, AlexNet, VGGNet, ResNet)
• 3.4 Working of CNN layers
• +4 more topics...
Unit IV • 14 Hrs
Unit IV - Recurrent Neural Networks (RNNs)
Syllabus Topics Covered:
• 4.1 Introduction to Recurrent Neural Networks
• 4.2 Types of Recurrent Neural Networks
• 4.3 Recurrent Neural Networks Architecture
• 4.4 Examples of sequential data: Text, time-series, and speech
• +6 more topics...
• 4.2 Types of Recurrent Neural Networks
• 4.3 Recurrent Neural Networks Architecture
• 4.4 Examples of sequential data: Text, time-series, and speech
• +6 more topics...
Unit V • 10 Hrs
Unit V - Deep Learning Applications, Platforms and Software Libraries
Syllabus Topics Covered:
• 5.1 Deep Learning Applications
• 5.1.1 Large-scale deep learning
• 5.1.2 Computer vision
• 5.1.3 Speech recognition
• +7 more topics...
• 5.1.1 Large-scale deep learning
• 5.1.2 Computer vision
• 5.1.3 Speech recognition
• +7 more topics...