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Amit Yadav
In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset.
Read more
In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset.
Read more
In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.
Prerequisites:
In order to be successful in this project, you should be familiar with python and convolutional neural networks.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/mmno5z/image
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What's inside
Syllabus
Image Classification with CNNs using Keras
In this hands-on project based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset. The same technique can be used to solve image classification problems on your own data as well.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners who want to build a strong foundation in image classification with CNNs using Keras and Tensorflow
Taught by experienced instructor Amit Yadav, who is known for their work in deep learning and neural networks
Focuses on developing practical skills through hands-on project-based learning
Utilizes Rhyme, a platform that provides instant access to pre-configured cloud desktops with necessary software and data
May require prior knowledge of Python and convolutional neural networks for successful completion
Access to the cloud desktop is limited to five times
Save this course
Save Image Classification with CNNs using Keras to your list so you can find it easily later:
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Reviews summary
Cnn image classification with keras
Learners say that this Image Classification with CNNs using Keras course effectively prepares learners for the practical application of CNNs or Convolutional Neural Networks in image processing using the Keras library.
Course offers clear explanations of CNNs and their applications.
"Excellent Guided Project. Very well explained and easy to understand."
"Learnt CNN and keras. Interesting interactive explanation."
"The project was straight to the point and the instructor did not waste any time talking about every basic code segment."
Learners with some general knowledge in CNNs find this course suitable and easy to follow.
"Its a good course for practicing your concepts."
"Easy to understand and it is good to start with for beginners."
"it was short n up to the mark, fully hands on and i came to know many new terms and their working."
Students applaud the guided project which they say provides practice using CNNs and Keras.
"This is a very good guided project."
"Good guided project."
"Great hands on CNN project."
Students complain that the course's reliance on the Rhyme platform leads to problems with typing certain symbols and functions properly.
"Rhyme interface is very bad and slow."
"This guided project uses a very cumbersome working environent on Rhyme."
"I couldn't type some symbols (= or []) with the common combinations of my keyboard."
Students with little to no knowledge of CNNs may struggle to follow along with the coding portions of the course.
"Need more explanation. Tough to understand every concept for beginners with little knowledge about CNN."
"I understand that it is quite hard to explain how to code these scripts since the first task, it is not suitable for the Python and CNN beginner at all."
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 Image Classification with CNNs using Keras with these
activities:
Watch video tutorials on CNNs
Show steps
Watching video tutorials can help you quickly learn the basics of CNNs
Browse courses on
CNN
Show steps
-
Find video tutorials on CNNs
-
Watch the video tutorials
-
Take notes on the key concepts
Compile and review course materials
Show steps
Compiling and reviewing materials will orient you to the course structure and help you to assess your prior preparation and knowledge
Show steps
-
Gather and sort your notes
-
Gather and sort your assignments
-
Gather and sort your quizzes
-
Gather and sort your study materials
Participate in discussion forums
Show steps
Participating in discussion forums can help you connect with other learners and get different perspectives on the course material
Show steps
-
Find discussion forums related to CNNs
-
Join the discussion forums
-
Participate in the discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Find a mentor who can help you with your CNN projects
Show steps
Mentors can provide guidance and support to help you learn and grow as a developer
Browse courses on
CNN
Show steps
-
Identify potential mentors
-
Reach out to potential mentors
-
Build a relationship with your mentor
Code a simple CNN in TensorFlow
Show steps
Practicing how to code a simple CNN will help you build skills that are essential for this course
Browse courses on
TensorFlow
Show steps
-
Review the TensorFlow documentation
-
Review the CNN architecture
-
Code the CNN in TensorFlow
-
Train the CNN on a small dataset
-
Evaluate the CNN's performance
Do practice exercises on CNNs
Show steps
Practice exercises can help you test your understanding of CNNs and identify areas where you need to improve
Browse courses on
CNN
Show steps
-
Find practice exercises on CNNs
-
Complete the practice exercises
-
Review your answers
Participate in Kaggle competitions
Show steps
Participating in competitions on Kaggle will challenge you to apply your skills and knowledge to real-world problems
Browse courses on
Machine Learning
Show steps
-
Find a competition that interests you
-
Read the competition description
-
Download the data
-
Build a model
-
Submit your model
Contribute to open-source projects related to CNNs
Show steps
Contributing to open-source projects can help you learn from others and improve your skills
Browse courses on
CNN
Show steps
-
Find open-source projects related to CNNs
-
Read the project documentation
-
Identify ways to contribute
-
Make your contributions
Watch video tutorials on CNNs
Show steps
Watching video tutorials can help you quickly learn the basics of CNNs
Browse courses on
CNN
Show steps
Show steps
Show steps
- Find video tutorials on CNNs
- Watch the video tutorials
- Take notes on the key concepts
Compile and review course materials
Show steps
Compiling and reviewing materials will orient you to the course structure and help you to assess your prior preparation and knowledge
Show steps
Show steps
Show steps
- Gather and sort your notes
- Gather and sort your assignments
- Gather and sort your quizzes
- Gather and sort your study materials
Participate in discussion forums
Show steps
Participating in discussion forums can help you connect with other learners and get different perspectives on the course material
Show steps
Show steps
Show steps
- Find discussion forums related to CNNs
- Join the discussion forums
- Participate in the discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Find a mentor who can help you with your CNN projects
Show steps
Mentors can provide guidance and support to help you learn and grow as a developer
Browse courses on
CNN
Show steps
Show steps
Show steps
- Identify potential mentors
- Reach out to potential mentors
- Build a relationship with your mentor
Code a simple CNN in TensorFlow
Show steps
Practicing how to code a simple CNN will help you build skills that are essential for this course
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review the TensorFlow documentation
- Review the CNN architecture
- Code the CNN in TensorFlow
- Train the CNN on a small dataset
- Evaluate the CNN's performance
Do practice exercises on CNNs
Show steps
Practice exercises can help you test your understanding of CNNs and identify areas where you need to improve
Browse courses on
CNN
Show steps
Show steps
Show steps
- Find practice exercises on CNNs
- Complete the practice exercises
- Review your answers
Participate in Kaggle competitions
Show steps
Participating in competitions on Kaggle will challenge you to apply your skills and knowledge to real-world problems
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a competition that interests you
- Read the competition description
- Download the data
- Build a model
- Submit your model
Contribute to open-source projects related to CNNs
Show steps
Contributing to open-source projects can help you learn from others and improve your skills
Browse courses on
CNN
Show steps
Show steps
Show steps
- Find open-source projects related to CNNs
- Read the project documentation
- Identify ways to contribute
- Make your contributions
Career center
Learners who complete Image Classification with CNNs using Keras will develop knowledge and skills
that may be useful to these careers:
Image Processing Engineer
Image Processing Engineer
Image Processing Engineers design, develop, and test image processing systems. They work on tasks such as image enhancement, image restoration, and image analysis. This course provides a foundation in CNNs, which are a powerful tool for image processing tasks. By understanding CNNs and how to train them, you can develop the skills needed to succeed as an Image Processing Engineer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers apply machine learning principles to solve real-world problems. They design, build, and train machine learning models to automate tasks, improve decision-making, and gain insights from data. This course provides a foundation in Convolutional Neural Networks (CNNs), a powerful type of machine learning model used for image classification and recognition. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Machine Learning Engineer.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and train deep learning models. They work on tasks such as natural language processing, computer vision, and speech recognition. This course provides a foundation in CNNs, which are a type of deep learning model. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Deep Learning Engineer.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, develop, and test computer vision systems. They work on tasks such as object detection, image segmentation, and facial recognition. This course provides a foundation in CNNs, which are the cornerstone of many computer vision systems. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Computer Vision Engineer.
Data Scientist
Data Scientist
Data Scientists use data to extract insights and knowledge. They analyze data to identify patterns, trends, and anomalies, and develop models to predict outcomes and make recommendations. This course provides a foundation in CNNs, which are commonly used in image classification and recognition tasks. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Data Scientist.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and test artificial intelligence systems. They work on tasks such as natural language processing, computer vision, and robotics. This course provides a foundation in CNNs, which are a type of artificial intelligence model. By understanding CNNs and how to train them, you can develop the skills needed to succeed as an Artificial Intelligence Engineer.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software systems. They work on tasks such as web development, mobile development, and data analysis. This course provides a foundation in CNNs, which are used in a variety of software applications. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Software Engineer.
Data Analyst
Data Analyst
Data Analysts use data to extract insights and knowledge. They analyze data to identify patterns, trends, and anomalies, and develop models to predict outcomes and make recommendations. This course provides a foundation in CNNs, which are a powerful tool for data analysis tasks. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Data Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They develop models to predict the performance of stocks, bonds, and other financial instruments. This course provides a foundation in CNNs, which can be used to analyze financial data. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Quantitative Analyst.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They develop models to predict outcomes and make recommendations based on data. This course provides a foundation in CNNs, which can be used to analyze statistical data. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Statistician.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They analyze data to identify opportunities, risks, and trends, and develop recommendations to improve business performance. This course provides a foundation in CNNs, which can be used to analyze image data. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Business Analyst.
Research Scientist
Research Scientist
Research Scientists conduct research to develop new knowledge and technologies. They work on a variety of topics, including medicine, physics, and computer science. This course provides a foundation in CNNs, which can be used to solve research problems. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Research Scientist.
Product Manager
Product Manager
Product Managers develop and manage products. They work with engineers, designers, and marketers to create products that meet the needs of customers. This course provides a foundation in CNNs, which can be used to develop image-based products. By understanding CNNs and how to train them, you can develop the skills needed to succeed as a Product Manager.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. They work with engineers and other technical experts to create documentation that is clear, concise, and accurate. This course may be полезны for Technical Writers who are documenting image classification or recognition systems. By understanding CNNs and how to train them, you can develop the skills needed to write effective documentation for such systems.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to define project goals, develop project plans, and track project progress. This course may be полезны for Project Managers who are working on projects involving image classification or recognition. By understanding CNNs and how to train them, you can develop the skills needed to successfully manage such projects.
For more career information including salaries, visit:
OpenCourser.com/course/mmno5z/image
Reading list
We've selected 12 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
Image Classification with CNNs using Keras.
Save
Provides a comprehensive overview of convolutional neural networks (CNNs), the most widely used type of deep learning model for image recognition. It covers the theoretical foundations of CNNs, as well as practical techniques for building and training CNN models.
Provides a comprehensive overview of machine learning, with a focus on the probabilistic perspective. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of machine learning.
Save
Provides a comprehensive overview of pattern recognition, including the theoretical foundations and practical techniques used in this field. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of pattern recognition.
Provides a comprehensive overview of pattern recognition and machine learning, including the theoretical foundations and practical techniques used in these fields. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of these fields.
Provides a comprehensive introduction to deep learning, including the underlying principles and techniques used to build and train deep learning models. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of this field.
Provides a comprehensive overview of machine learning, including the theoretical foundations and practical algorithms used in this field. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of machine learning.
Save
Provides a comprehensive overview of machine learning, including the theoretical foundations and practical techniques used in this field. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of machine learning.
Provides a practical guide to deep learning for computer vision tasks, such as image classification, object detection, and image segmentation. It covers the latest techniques and algorithms in this field, and provides practical examples of how to use them.
Provides a practical introduction to machine learning, with a focus on how to apply machine learning techniques to real-world problems. It valuable resource for beginners who want to learn how to use machine learning to solve problems in a variety of domains.
Provides a practical introduction to machine learning, with a focus on how to use popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for beginners who want to learn how to use machine learning to solve problems in a variety of domains.
Provides a practical introduction to machine learning, with a focus on how to use R to build and train machine learning models. It valuable resource for beginners who want to learn how to use machine learning to solve problems in a variety of domains.
Provides a practical introduction to machine learning, with a focus on how to use Python to build and train machine learning models. It valuable resource for beginners who want to learn how to use machine learning to solve problems in a variety of domains.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mmno5z/image
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Suitable for beginners who want to build a strong foundation in image classification with CNNs using Keras and Tensorflow
Taught by experienced instructor Amit Yadav, who is known for their work in deep learning and neural networks
Focuses on developing practical skills through hands-on project-based learning
Utilizes Rhyme, a platform that provides instant access to pre-configured cloud desktops with necessary software and data
May require prior knowledge of Python and convolutional neural networks for successful completion
Access to the cloud desktop is limited to five times
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