Getting started with TensorFlow 2 from Coursera

Welcome to this course on Getting started with TensorFlow 2!

In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models.

You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills.

At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will 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 central focus on ease of use for all users, from beginner to advanced level. This course is intended for both users who are completely new to Tensorflow, as well as users with experience in Tensorflow 1.x.

The prerequisite knowledge required in order to be successful in 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/feedforward and convolutional neural networks), activation functions, output layers, and optimisation.

What's inside

Syllabus

Introduction to TensorFlow

TensorFlow is one of the most popular libraries for deep learning, and it’s widely used today amongst researchers and professionals at all levels. In this week, you will get started with using TensorFlow on the Coursera platform and familiarise yourself with the course structure. You will also learn about some helpful resources when developing deep learning models in TensorFlow, including Google Colab. This week is really about getting everything set up, ready for diving into TensorFlow in the following week of the course.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Taught by Dr Kevin Webster, who is known for his work with TensorFlow

Develops skills and knowledge for deep learning models in Tensorflow essential to industry

Develops knowledge of TensorFlow, which is a popular choice for deep learning

Assumes proficiency in Python, machine learning concepts, and deep learning

May require paid subscriptions or exam fees

Reviews summary

Practical tensorflow 2 workflow

According to learners, 'Getting started with TensorFlow 2' offers a largely positive experience, particularly praised for its hands-on, practical approach and clear explanations of core TensorFlow 2 concepts. Students frequently highlight the useful labs, well-designed programming assignments, and the culminating Capstone Project, which helps solidify an end-to-end deep learning workflow. While generally well-received, some learners noted that the course, despite its title, requires a strong background in Python and machine learning fundamentals, making it more suited for those 'getting started' specifically with TF2 rather than deep learning in general. A few also mentioned that due to the rapid evolution of TensorFlow, some content might periodically need updates to remain fully current.

Assignments are effective, but some technical quirks were noted.

"The assignments were well-designed and reinforced the concepts effectively."

"The automated grader was a bit finicky, leading to frustration even when my code was logically correct."

"Occasionally the Colab notebooks had minor issues that needed troubleshooting on my end."

Requires solid Python and foundational machine learning knowledge.

"While it's titled 'Getting started,' I felt that the prerequisites were quite strict. If you're not already comfortable with Python and basic ML, you might struggle."

"It's more for 'getting started with TF2 specifically' rather than 'getting started with deep learning'."

"This course is a solid start for anyone looking to dive into TensorFlow 2, assuming I meet the prerequisites."

Instructors provide concise and easy-to-follow explanations.

"I found the content clear and concise, especially the modules on the Sequential API and callbacks."

"The instruction was top-notch, very clear and easy to follow. I gained a lot of confidence in using TF2."

"The explanations were thorough, and the course provided a solid foundation for TF2 development."

Gain practical skills through labs and a capstone project.

"This course is a fantastic introduction to TensorFlow 2. The hands-on labs were incredibly helpful, and I really appreciated the focus on practical application rather than just theory."

"The Capstone Project was a great way to bring everything together. It felt like a real-world application of the skills learned, and successfully completing it was very satisfying."

"This course delivers exactly what it promises: a practical, end-to-end workflow for TF2. The structure is logical, building from basics to a full project."

TensorFlow's rapid evolution means periodic updates are needed.

"Unfortunately, I found the course slightly outdated in certain practices by the time I completed it. TensorFlow evolves so fast, and some snippets or recommended approaches have already been superseded."

"The field moves quickly; some parts could benefit from refreshing with the very latest TF2 features or best practices."

"I noticed a few minor inconsistencies with the most recent TensorFlow versions and practices."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Getting started with TensorFlow 2 with these activities:

TensorFlow Tutorial Exercises

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Reinforce TensorFlow concepts by completing guided tutorials and exercises.

Browse courses on TensorFlow

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Follow step-by-step tutorials on TensorFlow's official website.
Practice coding exercises provided in the tutorials to solidify your understanding.
Debug your code and troubleshoot any errors encountered during the exercises.

Practice building deep learning models with TensorFlow

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Practice solving problems by building and implementing deep learning models, enhancing your proficiency with TensorFlow's features and architecture.

Browse courses on TensorFlow

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Complete the hands-on coding tutorials within the course materials
Create simple deep learning models on your own, using the skills learned in the course
Seek out additional resources and tutorials to further refine your understanding and skills

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Career center

Learners who complete Getting started with TensorFlow 2 will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

Deep Learning Engineers design, develop, and deploy deep learning models. They work with data scientists and machine learning engineers to identify the right deep learning algorithms for a given problem, and they develop and implement the models. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

See salaries and explore the career path for Deep Learning Engineer

Research Scientist

Research Scientists develop new technologies and products by conducting research and experiments. Their work may involve designing and conducting experiments, analyzing data, and developing new theories and models. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

See salaries and explore the career path for Research Scientist

Computer Vision Engineer

Computer Vision Engineers design, develop, and deploy computer vision models. They work with data scientists and machine learning engineers to identify the right computer vision algorithms for a given problem, and they develop and implement the models. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

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Natural Language Processing Engineer

Natural Language Processing Engineers design, develop, and deploy natural language processing models. They work with data scientists and machine learning engineers to identify the right natural language processing algorithms for a given problem, and they develop and implement the models. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They work with data scientists to identify the right machine learning algorithms for a given problem, and they develop and implement the models. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. They work with data to identify patterns, trends, and anomalies, and they use this information to make recommendations and predictions. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

See salaries and explore the career path for Data Scientist

Robotics Engineer

Robotics Engineers design, develop, and deploy robots. They work with data scientists and machine learning engineers to identify the right machine learning algorithms for a given problem, and they develop and implement the models. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

See salaries and explore the career path for Robotics Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. They work with users to gather requirements, and they design and implement software solutions that meet those requirements. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

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Data Analyst

Data Analysts use their knowledge of statistics, programming, and machine learning to extract insights from data. They work with data to identify patterns, trends, and anomalies, and they use this information to make recommendations and predictions. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use their knowledge of business and technology to identify and solve business problems. They work with stakeholders to gather requirements, and they develop and implement solutions that meet those requirements. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

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Marketing Manager

Marketing Managers are responsible for the development and execution of marketing campaigns. They work with marketers to create and implement campaigns that reach target audiences and achieve marketing goals. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

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Sales Manager

Sales Managers are responsible for the development and execution of sales strategies. They work with sales teams to achieve sales goals and objectives. This course can help you develop the skills and knowledge necessary to be successful in this role, such as how to build and train deep learning models, how to validate and evaluate models, and how to save and load models. Additionally, the course will give you hands-on experience with TensorFlow, one of the most popular deep learning libraries.

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Finance Manager

Finance Managers are responsible for the financial health of an organization. They work with accountants and other financial professionals to develop and implement financial strategies. This course may be useful for those who want to develop a foundation in deep learning, as it can help you understand the underlying concepts and algorithms of deep learning models.

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Human Resources Manager

Human Resources Managers are responsible for the development and implementation of human resources policies and programs. They work with employees and managers to ensure that the organization has a skilled and motivated workforce. This course may be useful for those who want to develop a foundation in deep learning, as it can help you understand the underlying concepts and algorithms of deep learning models.

See salaries and explore the career path for Human Resources Manager