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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 taking this course, students will acquire the skills and knowledge to develop fully customised deep learning models and workflows for any application. They will learn to use lower level APIs in TensorFlow to develop complex model architectures, customised layers and a flexible data workflow. They will also expand their knowledge of the TensorFlow APIs to include sequence models. Students will gain practical experience by putting concepts into practice in hands-on coding tutorials and programming assignments. At the end of the course, students will bring many of the concepts together in a Capstone Project where they will develop a custom neural translation model from scratch. The prerequisite knowledge required for this course is proficiency in the Python programming language (this course uses Python 3), knowledge of general machine learning concepts (such as overfitting & underfitting, supervised learning tasks, validation, regularisation and model selection) and a working knowledge of the field of deep learning including typical model architectures (MLP, CNN, RNN, ResNet) and concepts such as transfer learning, data augmentation and word embeddings.
lHow does this course contribute to professional growth?
This course on Customising your models with TensorFlow 2 provides an opportunity for professional growth. It allows learners to deepen their knowledge and skills with TensorFlow in order to develop fully customised deep learning models and workflows for any application. Through practical hands-on coding tutorials and a series of automatically graded programming assignments, learners will be able to put concepts that they learn about into practice and consolidate their skills. At the end of the course, learners will be able to develop a custom neural translation model from scratch. This course is a great opportunity for professionals to expand their knowledge of the TensorFlow APIs and gain a better understanding of the open source machine library.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education as it provides a comprehensive overview of TensorFlow 2 and its capabilities. It covers topics such as lower level APIs, complex model architectures, sequence models, practical coding tutorials, programming assignments, and a Capstone Project. It also requires prerequisite knowledge in Python programming, machine learning concepts, and deep learning.