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Image Classification with CNNs using Keras

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.

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.

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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

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

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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

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Watching video tutorials can help you quickly learn the basics of CNNs

Browse courses on CNN

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Find video tutorials on CNNs
Watch the video tutorials
Take notes on the key concepts

Compile and review course materials

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Compiling and reviewing materials will orient you to the course structure and help you to assess your prior preparation and knowledge

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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

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Participating in discussion forums can help you connect with other learners and get different perspectives on the course material

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Find discussion forums related to CNNs
Join the discussion forums
Participate in the discussions

Five other activities

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Show all eight activities

Find a mentor who can help you with your CNN projects

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Mentors can provide guidance and support to help you learn and grow as a developer

Browse courses on CNN

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Identify potential mentors
Reach out to potential mentors
Build a relationship with your mentor

Code a simple CNN in TensorFlow

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Practicing how to code a simple CNN will help you build skills that are essential for this course

Browse courses on TensorFlow

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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

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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

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Participating in competitions on Kaggle will challenge you to apply your skills and knowledge to real-world problems

Browse courses on Machine Learning

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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

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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

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 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.

See salaries and explore the career path for Image Processing 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.

See salaries and explore the career path for Machine 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.

See salaries and explore the career path for Deep Learning 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.

See salaries and explore the career path for Computer Vision Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Artificial Intelligence 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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Research Scientist

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Technical Writer

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.

See salaries and explore the career path for Project Manager

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.

Deep Learning

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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.

Image Classification with CNNs using Keras

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Cnn image classification with keras

Activities

Career center

Reading list

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