Neural Network Optimization Strategies is an intensive course designed to equip participants with the advanced skills necessary to enhance the performance of deep learning models. The curriculum emphasizes hands-on, project-based learning, allowing participants to engage directly with optimization techniques and tools. Throughout the course, learners will tackle real-world challenges and apply their knowledge to optimize neural networks effectively, preparing them for immediate application in professional settings.
Participants will explore a variety of optimization strategies, including hyperparameter tuning, regularization techniques, and advanced training methodologies. The course culminates in a final project where learners will implement their acquired skills to optimize a neural network for a specific application, with the opportunity to publish their findings in Cademix Magazine. This program not only fosters technical proficiency but also encourages collaboration and innovation among peers.
Introduction to Neural Networks and Optimization
Fundamentals of Gradient Descent and Its Variants
Hyperparameter Tuning Techniques
Regularization Methods: L1, L2, Dropout
Advanced Optimization Algorithms: Adam, RMSprop, and more
Transfer Learning and Fine-Tuning Strategies
Performance Metrics for Neural Network Evaluation
Implementing Neural Network Pruning and Quantization
Case Studies: Successful Neural Network Optimizations
Final Project: Optimizing a Neural Network for a Real-World Application