Hands-On Transfer Learning with TensorFlow 20
Step into the world of Transfer Learning with TensorFlow 2.0! Learn to adapt pre-trained models to new tasks and supercharge your AI projects. #TransferLearning #TensorFlow #AIInstitute ▼
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Course Feature
Cost:
Paid
Provider:
Udemy
Certificate:
Paid Certification
Language:
English
Start Date:
2020-06-16
Course Overview
❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [August 31st, 2023]
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
By the end of this course, learners will have acquired the skills and knowledge to implement transfer learning to solve different sets of machine learning problems. They will be able to reuse pre-trained models to train other models, and understand how and why transfer learning is extensively used in different deep learning domains. Learners will be able to build machine learning models and have mastered transferring with tfkeras, TensorFlow Hub, and TensorFlow Lite tools. They will also be able to implement practical use cases of transfer learning in CNN and RNN, such as using image classifiers, text classification, and sentimental analysis.
lHow does this course contribute to professional growth?
This course contributes to professional growth by providing a comprehensive overview of transfer learning with TensorFlow 20. It covers the fundamentals of transfer learning, how to implement it in CNN and RNN, and how to use pre-trained models to train other models. Through hands-on examples, learners will gain a better understanding of how transfer learning is used in different deep learning domains and how it can be applied to solve real-world problems. Learners will also benefit from the expertise of Margaret Maynard-Reid, a Google Developer Expert (GDE) for Machine Learning, contributor to the open-source ML framework TensorFlow, and an author of the official TensorFlow blog. By the end of the course, learners will have mastered the transfer learning process and be able to apply it to their own projects.
Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education as it provides hands-on examples of transfer learning with TensorFlow 20 and teaches how to implement practical use cases of transfer learning in CNN and RNN. It is taught by Margaret Maynard-Reid, a Google Developer Expert (GDE) for Machine Learning and contributor to the open-source ML framework TensorFlow. By the end of the course, students will have mastered transferring with tfkeras TensorFlow Hub and TensorFlow Lite tools.
Course Syllabus
Image Classifier from Scratch with TensorFlow 2.0
Transfer Learning with tf.keras
Transfer Learning with TensorFlow Hub
TFLite Model Maker
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