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Updated in [August 31st, 2023]

What does this course tell?
(Please note that the following overview content is from Alison)
This course will teach you how to develop deep learning models with TensorFlow 2, from building, training, evaluating and predicting with models, to validating models and including regularisation, implementing callbacks and saving and loading models. You will be guided through practical coding tutorials and have the opportunity to consolidate your skills with a series of automatically graded programming assignments. At the end of the course, you will bring many of the concepts together in a Capstone Project to develop an image classifier deep learning model from scratch. TensorFlow is an open source machine library and is one of the most widely used frameworks for deep learning. The release of TensorFlow 2 marks a step change in the product development with a focus on ease of use for all users. Prerequisite knowledge required for this course is proficiency in Python (using Python 3), knowledge of general machine learning concepts and a working knowledge of deep learning, including typical model architectures, activation functions, output layers and optimisation.

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 necessary to develop deep learning models with TensorFlow 2. This includes building, training, evaluating, and predicting with models using the Sequential API, validating models and including regularisation, implementing callbacks, and saving and loading models. Students will also gain practical experience by completing hands-on coding tutorials and programming assignments. At the end of the course, students will be able to bring many of the concepts together in a Capstone Project, where they will develop an image classifier deep learning model from scratch.

In order to be successful in this course, students should have 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 & feedforward and convolutional neural networks), activation functions, output layers, and optimisation.
lHow does this course contribute to professional growth?
This course on Getting Started with TensorFlow 2 provides professional growth opportunities for those with a working knowledge of the field of deep learning. It offers a comprehensive end-to-end workflow for developing deep learning models with TensorFlow, from building, training, evaluating, and predicting with models using the Sequential API, to validating models and including regularisation, implementing callbacks, and saving and loading models. Through practical hands-on coding tutorials and a series of automatically graded programming assignments, users will be able to put concepts into practice and consolidate their skills. The course culminates in a Capstone Project, where users will develop an image classifier deep learning model from scratch. With the release of TensorFlow 2, users of all levels, from beginner to advanced, can benefit from the product's focus on ease of use.

Is this course suitable for preparing further education?
This course is suitable for preparing further education in deep learning with TensorFlow 2. It covers a complete end-to-end workflow for developing deep learning models, and provides practical hands-on coding tutorials and programming assignments to help consolidate skills. It is intended for both users who are completely new to TensorFlow as well as users with experience in TensorFlow 1x. Prerequisite knowledge required in order to be successful in this course includes proficiency in the Python programming language, knowledge of general machine learning concepts, and a working knowledge of the field of deep learning.