Neural Networks - Where Do I Start

Overview

Explore neural network architectures, from basic to cutting-edge, and learn how to apply them to real-world problems. Gain insights on getting started with neural networks in your projects.

Syllabus

• Introduction to Neural Networks
History and Evolution
Key Concepts and Terminology
Biological Inspiration
• Fundamentals of Neural Networks
Neurons and Perceptrons
Activation Functions
Feedforward and Backpropagation
Loss Functions
• Basic Neural Network Architectures
Single-Layer Networks
Multi-Layer Perceptrons (MLP)
• Training Neural Networks
Data Preparation
Gradient Descent and Optimization Techniques
Overfitting and Regularization
• Advanced Architectures
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and LSTMs
Transfer Learning
• Hands-On Neural Networks
Implementing Neural Networks with Popular Frameworks (e.g., TensorFlow, PyTorch)
Building a Simple Neural Network Model
Model Evaluation and Tuning
• Real-World Applications
Image Classification
Natural Language Processing (NLP)
Time Series Prediction
• Current Trends and Future Directions
Explainable AI
Neural Architecture Search
Emerging Research and Technologies
• Getting Started with Your Projects
Identifying Problems Suitable for Neural Networks
Considerations for Real-World Deployment
Resources and Further Learning Paths
• Course Conclusion
Summary of Key Concepts
Q&A and Interactive Discussions
Next Steps in Neural Network Journeys

Subjects

Conference Talks