Learn how to fine-tune pre-trained CNN models like ResNet and VGG on custom datasets using TensorFlow for tasks such as image classification and object detection.
Learn how to apply transfer learning with pre-trained models like ResNet and BERT using TensorFlow, and fine-tune them for new tasks.
Learn about autoencoders, their architecture, and practical applications like anomaly detection, image denoising, and dimensionality reduction.
Learn about regularization techniques such as L1, L2 regularization, dropout, and early stopping to prevent overfitting in neural networks.
Learn the importance of hyperparameter tuning in machine learning, with an introduction to key hyperparameters and basic tuning methods like grid search and random search, along with practical tips to optimize model performance.