Dive into a comprehensive live-coded tutorial on Transfer Learning using TensorFlow. Explore the concept of leveraging pre-trained models to tackle new tasks efficiently, significantly reducing development time. Cover a wide range of topics including TensorFlow Hub, multi-class convolutional neural networks, data processing techniques, activation functions, pooling methods, performance optimization, and strategies to minimize overfitting. Learn about image augmentation, ResNet, and EfficientNet architectures. Gain hands-on experience with practical examples and in-depth explanations, from setting up the environment to analyzing images, creating data structures, normalizing data, and building multi-class models using ReLU activation. Follow along as the instructor demonstrates the entire process of creating and running a transfer learning model, providing valuable insights for both beginners and experienced practitioners in the field of machine learning and computer vision.