Transfer Learning with TensorFlow - Live Coding and Explanation

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.

Introduction
Overview
Setup
Data
Google Drive
Google Colab
Analyzing Images
Random Images
Data Structures
Normalize
MultiClass
Relu
Creating a model
Running the model