Hands-On Transfer Learning with TensorFlow 2.0
Transfer experience from models using TensorFlow 2.0
Created by Margaret Maynard-Reid
Explore how transfer learning lets you train deep neural networks faster and with less data by reusing knowledge from pre-trained models. You'll get hands-on with TensorFlow 2.0, learning its high-level API and how to tap into TensorFlow Hub for practical transfer learning tasks in both image and text domains.
Packt | May 2020 | 85 min
What You Will Learn
You'll work through clear, bite-sized video explanations and follow along with practical code examples in Colab. Each topic is broken down into manageable steps, so you can see how transfer learning works in real projects. Short quizzes help reinforce your understanding as you progress.
Key Features
- Apply transfer learning to image and text classification using TensorFlow 2.0
- Leverage pre-trained models to build deep learning applications with minimal data
- Master TensorFlow Hub and tf.keras for efficient model reuse and deployment
Target Audience
Designed for developers and data scientists with basic knowledge of machine learning and Python, this course is ideal if you want to expand your deep learning toolkit. If you're ready to move beyond training models from scratch and want to master transfer learning for real-world tasks, you'll find practical skills and insights here.
