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Laurence Moroney
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/s0i3x1/convolutional
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!
Read more
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!
Read more
Augmentation: A technique to avoid overfitting
You've heard the term overfitting a number of times to this point. Overfitting is simply the concept of being over specialized in training -- namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers. You'll learn all about that this week!
Transfer Learning
Building models for yourself is great, and can be very powerful. But, as you've seen, you can be limited by the data you have on hand. Not everybody has access to massive datasets or the compute power that's needed to train them effectively. Transfer learning can help solve this -- where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario. This is Transfer learning, and you'll look into that this week!
Multiclass Classifications
You've come a long way, Congratulations! One more thing to do before we move off of ConvNets to the next module, and that's to go beyond binary classification. Each of the examples you've done so far involved classifying one thing or another -- horse or human, cat or dog. When moving beyond binary into Categorical classification there are some coding considerations you need to take into account. You'll look at them this week!
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Convolutional Neural Networks in TensorFlow to your list so you can find it easily later:
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Reviews summary
Practical cnn in tf
learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library. It is a practical, hands-on course that focuses on the implementation of CNNs.
The course is well-structured and easy to follow, with short video lectures and coding assignments. The coding assignments are designed to reinforce the concepts learned in the lectures and to provide students with experience in building and training CNN models.
The course covers a wide range of topics, including:
* The basics of CNNs
* Different types of CNN architectures
* Training and evaluating CNNs
* Using CNNs for image classification and object detection
This course is a good choice for anyone who wants to learn how to use CNNs for image classification and object detection. It is also a good choice for anyone who wants to understand the underlying principles of CNNs.
The course features include:
* Short video lectures and coding assignments
* Step-by-step introduction to CNNs
* Implementation of CNNs
* Focus on image classification and object detection
"learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library."
"The course is well-structured and easy to follow, with short video lectures and coding assignments."
"The coding assignments are designed to reinforce the concepts learned in the lectures and to provide students with experience in building and training CNN models."
"The course covers a wide range of topics, including:
* The basics of CNNs
* Different types of CNN architectures
* Training and evaluating CNNs
* Using CNNs for image classification and object detection"
The course is well-structured and organized, making it easy to follow and learn from.
"learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library."
"The course is well-structured and easy to follow, with short video lectures and coding assignments."
The course is practical and hands-on, focusing on the implementation of CNNs. This provides students with experience in building and training CNN models.
"learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library."
"It is a practical, hands-on course that focuses on the implementation of CNNs."
One potential challenge with this course is that it primarily focuses on the implementation of CNNs, rather than providing a comprehensive overview of the theoretical foundations of CNNs. For learners who are new to CNNs, it may be helpful to supplement this course with additional resources that provide a more in-depth treatment of the theoretical aspects of CNNs. Additionally, some of the coding assignments may require learners to have some prior experience with Python and TensorFlow, so it may be helpful for learners to review these topics before starting the course.
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.
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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:
Machine Learning Engineer
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.
Data Scientist
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.
Software 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.
Data Analyst
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.
Financial Analyst
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.
Product Manager
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.
Quantitative Analyst
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.
Market Researcher
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.
User Experience Researcher
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.
Management Consultant
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.
Data Visualization Engineer
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.
Business 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.
Operations Research 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.
Statistician
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.
For more career information including salaries, visit:
OpenCourser.com/course/s0i3x1/convolutional
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Convolutional Neural Networks in TensorFlow.
Provides a practical guide to building and training deep learning models using Python. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and natural language processing.
Save
Provides a comprehensive overview of computer vision, including its theoretical foundations, its practical applications, and its ethical implications. It is particularly useful for those who are new to computer vision or who want to learn more about its foundations.
Provides a practical guide to building and training deep learning models for computer vision tasks. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and object detection.
Save
Provides a comprehensive overview of generative adversarial networks, including their theoretical foundations, their practical applications, and their ethical implications. It is particularly useful for those who are new to generative adversarial networks or who want to learn more about their foundations.
Provides a comprehensive overview of deep reinforcement learning, including its theoretical foundations, its practical applications, and its ethical implications. It is particularly useful for those who are new to deep reinforcement learning or who want to learn more about its foundations.
Provides a comprehensive overview of natural language processing with deep learning, including its theoretical foundations, its practical applications, and its ethical implications. It is particularly useful for those who are new to natural language processing with deep learning or who want to learn more about its foundations.
Provides a comprehensive overview of interpretable machine learning, including its theoretical foundations, its practical applications, and its ethical implications. It is particularly useful for those who are new to interpretable machine learning or who want to learn more about its foundations.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s0i3x1/convolutional
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Effort
4 weeks of study, 4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Laurence Moroney
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
DeepLearning.AI TensorFlow Developer. It's comprised of four courses:
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Convolutional Neural Networks in TensorFlow (viewing)
- Sequences, Time Series and Prediction
- Natural Language Processing in TensorFlow
Good to know
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
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