Deep Learning
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Deep Learning Syllabus — Unit-wise Content
The official GTU curriculum for Deep Learning (subject code 3164601) as prescribed for CS-DS · Semester 6 divides the course into 5 units, covering 22 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3164601)Unit 1 Introduction to Neural Network
- Neural Networks Overview: Biological inspiration
- Role of neural networks in machine learning
- Neural Network applications
Unit 2 Basic Concepts of Neural Networks
- Learning rules and activation functions
- Single-layer Perceptron and Learning
- Backpropagation Networks
- Feedforward Neural Networks
- encoders and decoders
- attention mechanism Optimization: Bias-Variance Tradeoff
- Regularization Techniques: L2 regularization
- Early stopping
- Gradient Descent and its variants (SGD
- Advanced optimizers
Unit 3 Foundations of Deep Learning
- History of Deep Learning
- Evolution and key milestones McCulloch Pitts Neuron and Thresholding Logic
- Multilayer Perceptron (MLPs)
- Dataset Augmentation: Techniques for improving training datasets
- Deep Learning Algorithms: CNN, LSTM, RNN
Unit 4 Advanced Optimization Techniques
- Injecting Noise at Input
- Ensemble methods
- Dropout: Preventing overfitting in neural networks Explainable AI in Deep Learning
Unit 5 Reinforcement Learning Basics
- Introduction to reinforcement learning concepts and applications Recent trends in Deep Learning architectures
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