Convolutional Neural Networks in TensorFlow from Coursera

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.

The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

What's inside

Syllabus

Exploring a Larger Dataset

In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, and you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Builds upon the foundational principles of Machine Learning and Deep Learning, expanding into practical applications and hands-on implementation

Taught by Laurence Moroney, a recognized expert in TensorFlow and AI development

Provides hands-on labs and interactive materials, enabling learners to apply concepts in a practical setting

Suitable for software developers who seek to develop scalable, AI-powered algorithms using TensorFlow

Covers advanced techniques for improving computer vision models, including image augmentation and transfer learning

Requires familiarity with basic TensorFlow and image classification concepts, recommending prerequisite courses for stronger foundational knowledge

Reviews summary

Practical convnets with tensorflow

According to learners, this course provides a solid practical foundation in applying Convolutional Neural Networks using TensorFlow. Many appreciate the hands-on coding exercises which help solidify theoretical concepts. The course content, covering techniques like augmentation and transfer learning with datasets like Cats and Dogs, is seen as highly relevant for real-world applications. While the lectures are generally clear, some found certain sections challenging, particularly if their prerequisites in Python or basic ML weren't strong. Overall, it is considered a great next step after Course 1, building effectively on prior knowledge.

Assumes baseline knowledge.

"Make sure you have a good understanding of Python and basic neural networks before starting."

"I struggled a bit with the assignments when my Python skills weren't as strong as needed."

"Assumes familiarity with concepts from previous courses and basic ML."

"Requires a decent foundation to fully grasp everything quickly."

Lectures are generally easy to follow.

"The explanations were generally clear and easy to understand, especially for complex topics."

"I found the instructor's way of explaining the concepts very helpful."

"Lectures break down the material into manageable segments."

"The theoretical parts were explained clearly before diving into the code."

Good progression from Course 1.

"This course is a great next step after taking the first course in the specialization."

"It built effectively on the introduction to TensorFlow provided in Course 1."

"I felt the concepts flowed well, assuming the knowledge from the previous module."

"It assumes you have completed the prior course, which makes sense and allows it to go deeper."

Topics are useful for practical application.

"Covering image augmentation and transfer learning was very useful for building real-world models."

"The content on handling larger datasets like Cats and Dogs is directly applicable."

"I appreciated learning techniques to prevent overfitting and improve model performance."

"The topics felt current and relevant to modern computer vision tasks."

Practical exercises are highly valuable.

"The hands-on coding and projects are the strongest part of the course for me, providing practical experience."

"I found the coding assignments particularly helpful in applying the concepts taught in the lectures."

"Doing the labs myself really solidified my understanding of how ConvNets work in TensorFlow."

"The practical exercises were well-designed and crucial for learning."

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 Convolutional Neural Networks in TensorFlow with these activities:

Seek guidance from a mentor or experienced professional

Show steps

Connect with a mentor or experienced professional for personalized guidance and support throughout your learning journey.

Browse courses on Deep Learning

Show steps

Identify potential mentors who have expertise in deep learning and TensorFlow.
Reach out to mentors and request their guidance.
Regularly schedule meetings or calls to discuss your progress and challenges.
Follow the advice and recommendations provided by your mentor.

Review Convolutional Neural Network (CNN) basics

Show steps

Refresh your understanding of CNN basics to strengthen your foundation for advanced concepts.

Browse courses on Convolutional Neural Networks

Show steps

Review course materials or online resources on CNNs.
Practice implementing simple CNNs using TensorFlow or other deep learning frameworks.

Participate in online discussion forums or study groups

Show steps

Engage with peers to discuss concepts, ask questions, and share knowledge, fostering a collaborative learning environment.

Browse courses on Deep Learning

Show steps

Join online discussion forums or study groups.
Actively participate in discussions, ask questions, and share your insights.
Collaborate with peers on projects or assignments.
Provide support and feedback to others in the group.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Practice coding exercises

Show steps

Practice coding exercises to solidify your understanding of deep learning concepts and TensorFlow.

Browse courses on Deep Learning

Show steps

Set aside time daily to work on coding exercises.
Start with the basic exercises and gradually move on to more complex ones.
Refer to the course materials for guidance and support.
Seek help from the online community or instructors if you encounter difficulties.

Follow tutorials on advanced deep learning techniques

Show steps

Explore advanced deep learning techniques by following tutorials and online resources.

Browse courses on Deep Learning

Show steps

Identify reputable sources for deep learning tutorials.
Choose a specific topic that you would like to learn more about.
Follow the instructions and complete the exercises in the tutorials.
Experiment with different techniques and apply them to your own projects.

Develop a transfer learning project

Show steps

Apply transfer learning to a real-world problem to enhance your understanding and practical skills.

Browse courses on Transfer Learning

Show steps

Identify a problem that can benefit from transfer learning.
Choose a pre-trained model that is relevant to your problem.
Fine-tune the pre-trained model on your own dataset.
Evaluate and optimize your transfer learning model.
Deploy your model for practical usage.

Build a Convolutional Neural Network (CNN) model

Show steps

Build a CNN model to apply your knowledge of image classification and improve your hands-on experience.

Browse courses on Convolutional Neural Networks

Show steps

Choose a dataset and prepare it for training.
Design and implement a CNN architecture.
Train and evaluate your CNN model.
Optimize your model for better accuracy and efficiency.
Deploy your model for practical usage.

Create a tutorial or blog post on a specific deep learning topic

Show steps

Create a tutorial or blog post to share your knowledge and understanding of deep learning and TensorFlow.

Browse courses on Deep Learning

Show steps

Identify a topic that you are comfortable with.
Research and gather information from credible sources.
Organize your content in a logical and engaging manner.
Write and edit your tutorial or blog post.
Publish and promote your content to reach a wider audience.

Career center

Learners who complete Convolutional Neural Networks in TensorFlow will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists analyze data to identify trends and patterns, and develop models to predict future outcomes. This course can help you build the skills you need to become a Data Scientist, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Data Scientist, you would be responsible for collecting, cleaning, and analyzing data to identify trends and patterns. You would also develop models to predict future outcomes and make recommendations. If you are interested in a career in data science, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you build the skills you need to become a Machine Learning Engineer, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Machine Learning Engineer, you would be responsible for designing, developing, and deploying machine learning models to solve real-world problems. If you are interested in a career in machine learning, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. This course can help you build the skills you need to become a Software Engineer, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Software Engineer, you would be responsible for designing, developing, and maintaining software systems. If you are interested in a career in software engineering, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help you build the skills you need to become a Data Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Data Analyst, you would be responsible for collecting, cleaning, and analyzing data to identify trends and patterns. If you are interested in a career in data analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to identify opportunities and solve problems. This course can help you build the skills you need to become a Business Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Business Analyst, you would be responsible for using data to identify opportunities and solve problems. If you are interested in a career in business analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians collect, analyze, and interpret data. This course can help you build the skills you need to become a Statistician, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Statistician, you would be responsible for collecting, analyzing, and interpreting data. If you are interested in a career in statistics, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help you build the skills you need to become a Quantitative Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Quantitative Analyst, you would be responsible for using mathematical and statistical models to analyze financial data. If you are interested in a career in quantitative analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. This course can help you build the skills you need to become an Operations Research Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As an Operations Research Analyst, you would be responsible for using mathematical and statistical models to solve business problems. If you are interested in a career in operations research, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Operations Research Analyst

Management Consultant

Management Consultants help organizations improve their performance. This course can help you build the skills you need to become a Management Consultant, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Management Consultant, you would be responsible for helping organizations improve their performance. If you are interested in a career in management consulting, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Management Consultant

Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. This course can help you build the skills you need to become a Financial Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Financial Analyst, you would be responsible for analyzing financial data to make investment recommendations. If you are interested in a career in financial analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers collect and analyze data about consumers and markets. This course can help you build the skills you need to become a Market Researcher, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Market Researcher, you would be responsible for collecting and analyzing data about consumers and markets. If you are interested in a career in market research, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Market Researcher

Product Manager

Product Managers are responsible for the development and launch of new products. This course can help you build the skills you need to become a Product Manager, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Product Manager, you would be responsible for the development and launch of new products. If you are interested in a career in product management, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Product Manager

Data Visualization Engineer

Data Visualization Engineers design and develop data visualizations to communicate data insights. This course can help you build the skills you need to become a Data Visualization Engineer, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Data Visualization Engineer, you would be responsible for designing and developing data visualizations to communicate data insights. If you are interested in a career in data visualization, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Data Visualization Engineer

User Experience Researcher

User Experience Researchers study how users interact with products and services. This course can help you build the skills you need to become a User Experience Researcher, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a User Experience Researcher, you would be responsible for studying how users interact with products and services. If you are interested in a career in user experience research, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for User Experience Researcher