Overview
In this 1 hr course, you'll explore the power of transfer learning by using pre-trained deep learning models with TensorFlow 2.0's new features, focusing on practical implementations for image and text classification. This course provides hands-on examples to master advanced transfer learning techniques for solving real-world problems effectively.
What I will be able to do after this course
- Learn how to build image classifiers using CNNs and TensorFlow 2.0.
- Implement transfer learning to improve model performance with minimal data.
- Understand the use of TensorFlow Hub for leveraging pre-trained models.
- Develop sentiment analysis systems with RNNs utilizing TensorFlow's capabilities.
- Master the tools like tf.keras and TensorFlow Lite for advanced applications.
Course Instructor(s)
Margaret Maynard-Reid, a Google Developer Expert in Machine Learning, brings extensive knowledge and experience in this domain. With a focus on making complex topics accessible, she combines lectures with practical demonstrations, ensuring learners gain hands-on skills in transfer learning. Her approachable teaching style helps learners apply concepts effectively.
Who is it for?
This course is ideal for machine learning enthusiasts looking to advance their skills by mastering transfer learning techniques. Learners should have a basic understanding of Python and machine learning. Whether you're aiming to enhance your practical deep learning skills or implement transfer learning in real-world scenarios, this course caters to your needs.
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