Customising your models with TensorFlow 2 from Coursera

Welcome to this course on Customising your models with TensorFlow 2!

In this course you will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence 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 a custom neural translation 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 follows on directly from the previous course Getting Started with TensorFlow 2. The additional 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, CNN, RNN, ResNet), and concepts such as transfer learning, data augmentation and word embeddings.

What's inside

Syllabus

The Keras functional API

TensorFlow offers multiple levels of API for constructing deep learning models, with varying levels of control and flexibility. In this week you will learn to use the functional API for developing more flexible model architectures, including models with multiple inputs and outputs. You will also learn about Tensors and Variables, as well as accessing and using inner layers within a model. The programming assignment for this week will put these techniques this into practice with a transfer learning application on the dogs and cats image dataset.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Suitable for those interested in machine learning, particularly TensorFlow, and with knowledge of Python programming

Focuses on customizing models and workflow, a key skill for deep learning development

Involves practical coding tutorials and programming assignments

Led by instructor with expertise in TensorFlow and deep learning

Builds upon knowledge from a previous course, requiring proficiency in Python and machine learning concepts

Requires working knowledge of deep learning, including model architectures and concepts

Reviews summary

Advanced tensorflow 2 model customization

According to learners, this course offers a deep dive into custom model building and advanced TensorFlow 2 APIs. Students find the practical, hands-on coding tutorials and programming assignments highly effective for applying concepts, culminating in a challenging Capstone Project. The course provides robust coverage of data pipelines with tf.data and delves into sequence modeling techniques. However, prospective students should be aware of the demanding prerequisites, including strong Python, ML, and deep learning knowledge, as the pace can be challenging for those not adequately prepared. It's ideal for those seeking to extend their TensorFlow skills beyond the basics.

Covers efficient data workflows with tf.data.

"The section on tf.data is incredibly powerful and well-explained."

"I learned to build flexible and efficient data pipelines."

"Understanding data augmentation on the fly was very valuable."

Emphasizes practical application through coding.

"The hands-on coding tutorials helped solidify concepts."

"I found the programming assignments very useful for practice."

"The Capstone Project is a great way to apply everything learned."

Explores advanced model building and low-level API usage.

"I appreciated learning to build truly custom models."

"The deep dive into lower-level APIs is exactly what I needed."

"This course empowers you to design complex architectures."

The course progresses quickly through complex topics.

"The pace was quite fast, especially in the later weeks."

"I found some concepts required extra independent study to fully grasp."

"It's a demanding course, prepare for a steep learning curve."

Requires strong prior knowledge in Python and ML/DL.

"This course is definitely not for beginners; ensure you meet the prerequisites."

"I struggled because my foundational knowledge wasn't strong enough."

"Highly recommend completing the previous course before starting this one."

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 Customising your models with TensorFlow 2 with these activities:

Review Python programming basics

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Refresh your Python programming skills to ensure a strong foundation for the course, minimizing potential roadblocks.

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Review basic Python syntax and data structures
Practice writing simple Python programs

Join a study group to work on course assignments

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Collaborate with peers in a study group to work on course assignments, strengthening your understanding through discussion and teamwork.

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Identify peers who are interested in forming a study group
Set regular meeting times and create a shared workspace
Work together to complete assignments and prepare for exams

Explore the TensorFlow Model Garden

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Explore the TensorFlow Model Garden to discover pre-trained models and learn how to apply them to your own projects, expanding your knowledge of real-world applications of TensorFlow.

Browse courses on Pre-Trained Models

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Browse the TensorFlow Model Garden and identify relevant models
Follow tutorials to implement pre-trained models in your own projects
Experiment with different pre-trained models to optimize results

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Work through TensorFlow tutorials on data pipelines

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Enhance your data handling skills by working through TensorFlow tutorials on data pipelines, gaining proficiency in data loading, transformation, and augmentation techniques.

Browse courses on TensorFlow

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Review TensorFlow's data pipeline API
Implement data pipelines for real-world datasets
Experiment with different data augmentation techniques

Complete practice exercises on sequence modelling

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Enhance your understanding of sequence modelling techniques by completing practice exercises, deepening your knowledge of LSTM and GRU networks.

Browse courses on Sequence Modelling

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Review concepts of sequence modelling
Implement LSTM and GRU networks in TensorFlow
Apply sequence modelling to real-world datasets

Build a residual network from scratch

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Build a residual network from scratch using the knowledge acquired in the course to solidify your understanding of advanced deep learning model architectures.

Browse courses on Residual Networks

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Design the residual network architecture
Implement the residual network using TensorFlow 2
Train the residual network on a dataset
Evaluate the performance of the residual network

Write a blog post about your experience with TensorFlow 2

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Share your experience with TensorFlow 2 by writing a blog post that highlights the key concepts you learned, the challenges you faced, and the projects you built. This will help you solidify your understanding and potentially assist other learners.

Browse courses on TensorFlow 2

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Brainstorm ideas and outline the blog post
Write the first draft of the blog post
Edit and revise the blog post
Publish the blog post and promote it

Contribute to an open-source TensorFlow project

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Gain practical experience and contribute to the TensorFlow community by participating in an open-source project, applying your skills to real-world scenarios.

Browse courses on TensorFlow

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Identify a suitable TensorFlow open-source project
Review the project's documentation and codebase
Make contributions to the project
Collaborate with project maintainers

Career center

Learners who complete Customising your models 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 a variety of data sources and deep learning algorithms. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Researcher

Machine Learning Researchers design, develop, and evaluate machine learning algorithms. They work with a variety of data sources and machine learning techniques. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Machine Learning Researcher.

See salaries and explore the career path for Machine Learning Researcher

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They work with a variety of data sources and machine learning algorithms. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

Natural Language Processing Engineers design, develop, and deploy natural language processing models. They work with a variety of data sources and natural language processing algorithms. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Natural Language Processing Engineer.

See salaries and explore the career path for Natural Language Processing Engineer

Software Engineer

As a Software Engineer, you'll design, develop, implement, and maintain software systems. You'll work with a variety of programming languages and technologies. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Software Engineer.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They work with a variety of data sources and quantitative analysis techniques. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Quantitative Analyst.

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Data Scientist

Data Scientists use data to solve business problems. They work with a variety of data sources and data analysis techniques. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Data Scientist.

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Computer Vision Engineer

Computer Vision Engineers design, develop, and deploy computer vision models. They work with a variety of data sources and computer vision algorithms. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Computer Vision Engineer.

See salaries and explore the career path for Computer Vision Engineer

Data Engineer

Data Engineers design, develop, and implement data pipelines. They work with a variety of data sources and data engineering techniques. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Data Engineer.

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Data Analyst

Data Analysts use data to solve business problems. They work with a variety of data sources and data analysis techniques. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Data Analyst.

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Software Developer

Software Developers design, develop, and implement software systems. They work with a variety of programming languages and technologies. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Software Developer.

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Speech Recognition Engineer

Speech Recognition Engineers design, develop, and deploy speech recognition models. They work with a variety of data sources and speech recognition algorithms. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Speech Recognition Engineer.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. They work with a variety of data sources and operations research techniques. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as an Operations Research Analyst.

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Business Analyst

Business Analysts use data to solve business problems. They work with a variety of data sources and business analysis techniques. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Business Analyst.

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Web Developer

Web Developers design, develop, and implement websites and web applications. They work with a variety of programming languages and technologies. This course will help you build a strong foundation in TensorFlow, one of the most widely used frameworks for deep learning. You'll learn how to develop fully customized deep learning models and workflows for any application. This will give you a competitive edge in the job market and help you succeed in your career as a Web Developer.

See salaries and explore the career path for Web Developer