Evolution of Neural Networks: Zero to Hero

Overview

Syllabus

• Introduction to Neural Networks
Overview of Neural Networks and Their Importance
Brief History and Milestones in Neural Network Development
• The Birth of Neural Networks
McCulloch-Pitts Neurons
Initial Models and Their Limitations
• Rise of Supervised Learning Models
Perceptrons
Single-layer Perceptrons
Perceptron Learning Algorithm
ADALINE (Adaptive Linear Neuron)
Delta Rule
Differences Between Perceptron and ADALINE
• Hopfield Networks
Recurrent Neural Networks and Hopfield's Contribution
Hopfield Network Dynamics and Applications
• Boltzmann Machines
Introduction to Stochastic Models
Energy-Based Models in Neural Networks
Restricted Boltzmann Machines
• From Shallow to Deep Learning
Multilayer Perceptrons (MLP)
Architecture of MLPs
Activation Functions
Introduction to Backpropagation
Training Multilayer Perceptrons
Challenges and Solutions in Training
• Advanced Topics in Neural Networks (Optional)
Deep Learning and Modern Innovations
Other Notable Models and Variations
• Conclusion and Future Directions
Recap of Major Developments
Emerging Trends in Neural Networks
• Course Wrap-up
Summary and Key Takeaways
Additional Resources and Further Reading

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Subjects

Computer Science