Getting started with TensorFlow 2
Master TensorFlow 2 for deep learning: Build, train, and deploy neural networks using Keras API. Perfect for ML practitioners upgrading skills.
Master TensorFlow 2 for deep learning: Build, train, and deploy neural networks using Keras API. Perfect for ML practitioners upgrading skills.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full TensorFlow 2 for Deep Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.9
(564 ratings)
36,717 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Build and train deep learning models using Sequential API
Implement model validation and regularization techniques
Use callbacks for monitoring and model optimization
Master model saving, loading, and deployment workflows
Develop image classification models from scratch
Skills you'll gain
This course includes:
4.2 Hours PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course teaches the complete workflow for developing deep learning models with TensorFlow 2. Students learn to build, train, evaluate and predict with models using the Sequential API, implement validation and regularization techniques, work with callbacks, and manage model saving and loading. The curriculum includes hands-on coding tutorials and practical assignments, culminating in a capstone project developing an image classifier. The course covers both fundamental concepts and advanced features of TensorFlow 2, making it suitable for beginners and experienced practitioners.
Introduction to TensorFlow
Module 1 · 2 Hours to complete
The Sequential model API
Module 2 · 6 Hours to complete
Validation, regularisation and callbacks
Module 3 · 6 Hours to complete
Saving and loading models
Module 4 · 6 Hours to complete
Capstone Project
Module 5 · 3 Hours to complete
Fee Structure
Instructor
Innovating Music with Machine Learning at Imperial College London
Kevin Webster is a Senior Teaching Fellow in the Department of Mathematics at Imperial College London, where he earned his PhD in dynamical systems in 2003. His research focuses on integrating machine learning techniques with numerical approximation challenges in dynamical systems, as well as leveraging machine learning and deep learning models for music applications, including music generation and listening. Notably, he developed the core AI for the commercial music audio search engine Figaro.
Testimonials
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4.9 course rating
564 ratings
Frequently asked questions
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