Customising your models with TensorFlow 2
Master advanced TensorFlow 2 features to create custom deep learning models, including sequence models and flexible architectures.
Master advanced TensorFlow 2 features to create custom deep learning models, including sequence models and flexible architectures.
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.
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Instructors:
English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Create complex model architectures using Keras functional API
Implement custom training loops and layers
Develop efficient data pipelines with tf.data
Build sequence models for natural language processing
Design flexible model architectures for specific applications
Skills you'll gain
This course includes:
5.6 Hours PreRecorded video
3 assignments, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course explores advanced TensorFlow 2 features for developing customized deep learning models. Students learn to use the Keras functional API, implement flexible data pipelines, and create sequence models. The curriculum covers custom layers, model subclassing, and automatic differentiation, culminating in a capstone project developing a neural translation model.
The Keras functional API
Module 1 · 6 Hours to complete
Data Pipeline
Module 2 · 6 Hours to complete
Sequence Modelling
Module 3 · 5 Hours to complete
Model subclassing and custom training loops
Module 4 · 5 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.
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