EXPERT TALK SERIES ON CONVOLUTIONAL NEURAL NETWORK: TRAINING, TESTING AND EVALUATIONS , KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

Understanding CNN Fundamentals: The fundamental concepts behind CNNs, including convolutional layers, pooling, and activation functions.

Training Techniques: Learn various techniques for training CNNs, including backpropagation, optimization algorithms, and weight initialization.

Testing and Evaluation:The proficient in testing and evaluating CNN models using metrics like accuracy, precision, recall, F1 score, and ROC-AUC.

Hyperparameter Tuning: Understanding how to effectively tune hyperparameters, such as learning rates, batch sizes, and model architecture, to optimize CNN performance.

Data Preprocessing: Learning about data preprocessing techniques like normalization, augmentation, and handling imbalanced datasets to improve CNN performance.

Transfer Learning: Understanding how to leverage pre-trained CNN models for specific tasks and fine-tune them.

Handling Overfitting: Techniques for preventing overfitting in CNNs, such as dropout, regularization, and early stopping.

Advanced Architectures: Exploring more advanced CNN architectures like VGG, ResNet, and Inception and understanding their advantages and use cases.

Practical Implementation: Hands-on experience with implementing CNNs using popular deep learning frameworks like TensorFlow or PyTorch.

Real-world Applications: Demonstrating how CNNs are applied in various domains, such as computer vision, natural language processing, and healthcare.

Research Trends: Keeping participants informed about the latest research trends and developments in the field of CNNs.

Case Studies: Analyzing real-world case studies and projects that highlight the practical use of CNNs in solving specific problems.