Introduction to Artificial neural network
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Introduction to Artificial neural network Syllabus — Unit-wise Content
The official GTU curriculum for Introduction to Artificial neural network (subject code 3160315) as prescribed for Biomed · Semester 6 divides the course into 5 units, covering 43 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 (3160315)Unit 1 Introduction to Artificial Neural Network
- Humans and Computers
- Organization of the Brain
- Biological Neuron
- Biological Neuron Model
- Characteristics of ANN, McCulloch-Pitts Model
- Historical Developments
- Potential Applications of ANN
Unit 2 Essentials of Artificial Neural Networks
- Artificial Neuron Model
- Operations of Artificial Neuron
- Types of Neuron Activation Function
- ANN Architectures (Perceptron
- Classification Taxonomy of ANN – Connectivity
- Neural Dynamics (Activation and Synaptic)
- Learning Strategy (Supervised
- Learning Rules
- Types of Application
Unit 3 Single Layer Network and Multi-layer Network
- Perceptron Models: Discrete
- Continuous and Multi-Category
- Training Algorithms Discrete and Continuous Perceptron Networks
- Perceptron Convergence theorem
- Limitations of the Perceptron Model
- Generalized Delta Rule
- Derivation of Backpropagation (BP) Training
- Summary of Backpropagation Algorithm
- Kolmogorov Theorem
- Learning Difficulties and Improvements
Unit 4 Associative Memories
- Paradigms of Associative Memory
- Pattern Mathematics
- Hebbian Learning
- General Concepts of Associative Memory (Associative Matrix
- Association Rules
- Hamming Distance
- The Linear Associator
- Matrix Memories
- Content Addressable Memory)
- Bidirectional Associative Memory (BAM) Architecture
- BAM Training Algorithms: Storage and Recall Algorithm
- BAM Energy Function
- Proof of BAM Stability Subject Code: 3160315 w.e.f
Unit 5 Theorem Architecture of Hopfield Network
- Discrete and Continuous versions
- Storage and Recall Algorithm
- Stability Analysis
- Capacity of the Hopfield Network Summary and Discussion of Instance/Memory Based Learning Algorithms
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