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Implement Image Recognition with a Convolutional Neural Network
Pratheerth Padman
Pratheerth Padman
Image recognition is used in a wide variety of ways in our daily lives. This course will teach you how to tune and implement convolutional neural networks in order to implement image recognition and classification on a business case.
Read more
Image recognition is used in a wide variety of ways in our daily lives. This course will teach you how to tune and implement convolutional neural networks in order to implement image recognition and classification on a business case.
Read more
Image recognition is used in a wide variety of ways in our daily lives. This course will teach you how to tune and implement convolutional neural networks in order to implement image recognition and classification on a business case.
Image recognition has an extensive and important impact on our daily lives. From unlocking phones using facial recognition to detecting anomalies in chest-x rays, it is everywhere.
In this course, Implement Image Recognition with a Convolutional Neural Network, you’ll understand how to implement image recognition and classification on your very own dataset.
First, you’ll be introduced to the problem and dataset. Then, you’ll learn how to explore and prepare the dataset for the next step. Next, you’ll see how to build, train, and test a neural network on the dataset. Finally, you’ll explore how image augmentation and transfer learning help to lift the performance metrics involved in your solution.
When you’re finished with this course, you’ll have the knowledge required to implement image recognition on any dataset of your choice.
Register for this course and see more details by visiting:
OpenCourser.com/course/hkgoig/implement
What's inside
Syllabus
Course Overview
Exploring and Preparing a Dataset for Image Recognition
Training a Convolutional Neural Network to Classify Images
Improving Performance of the Convolutional Neural Network
Read more
Syllabus
Course Overview
Exploring and Preparing a Dataset for Image Recognition
Training a Convolutional Neural Network to Classify Images
Improving Performance of the Convolutional Neural Network
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the foundations and application of convolutional neural networks for image recognition
Suitable for beginners with little to no experience in image recognition or neural networks
Practical course that emphasizes hands-on implementation and experimentation
Covers essential image augmentation techniques to improve model performance
Provides a strong foundation for further exploration in image recognition and deep learning
May require additional resources or background knowledge for learners with no prior experience in Python or machine learning
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Activities
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Implement Image Recognition with a Convolutional Neural Network.
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Career center
Learners who complete Implement Image Recognition with a Convolutional Neural Network will develop knowledge and skills
that may be useful to these careers:
Image Recognition Scientist
Image Recognition Scientist
An Image Recognition Scientist is a researcher or scientist who develops and tests image recognition algorithms or software. With a solid background in image processing and neural networks, this course would help build a foundation in the real-world application of image recognition.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer designs and builds computer systems that can interpret and understand visual inputs. The deep dive into how to train neural networks for image recognition in this course would be highly relevant to working in this role.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer specializes in building, deploying, and maintaining machine learning models. This course, with its emphasis on tuning and implementing convolutional neural networks for image recognition, would prove essential to a Machine Learning Engineer.
Data Scientist
Data Scientist
A Data Scientist uses various techniques to extract insights from data to inform business decisions. By the end of this course, you would have mastered building and training neural networks, a critical skill for many Data Scientists.
Software Engineer
Software Engineer
A Software Engineer works to design, develop, and maintain computer systems or software applications. This course covers the theoretical and practical fundamentals of building a convolutional neural network for image recognition, which would be a useful skill for a Software Engineer.
Algorithm Developer
Algorithm Developer
An Algorithm Developer designs and creates algorithms for various problem domains. By providing you with a comprehensive overview of building and applying neural networks for image recognition, this course is highly relevant to the field of Algorithm Development.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
An Artificial Intelligence Researcher develops new theories, models, and algorithms for Artificial Intelligence systems. This course would be useful for an Artificial Intelligence Researcher as it provides in-depth knowledge of the design and implementation of convolutional neural networks for image recognition.
Computer Graphics Software Developer
Computer Graphics Software Developer
A Computer Graphics Software Developer specializes in developing computer software to create or manipulate images. This course provides foundational knowledge on building and training convolutional neural networks for image recognition, which would be a valuable skill for a Computer Graphics Software Developer.
Software Architect
Software Architect
A Software Architect is responsible for overseeing the overall architecture and design of software systems. The course's focus on building and implementing convolutional neural networks for image recognition would be useful to a Software Architect seeking to build complex image-processing software systems.
Computer Hardware Engineer
Computer Hardware Engineer
A Computer Hardware Engineer designs and develops computer hardware systems. The course provides knowledge on building and training convolutional neural networks for image recognition, which would be useful for designing specialized hardware for image processing.
Security Engineer
Security Engineer
A Security Engineer is responsible for securing computer networks, devices, and data. By covering the concepts of convolutional neural networks and image recognition, this course may be useful for Security Engineers seeking to develop or implement image-based security systems.
Economist
Economist
An Economist studies the production, distribution, and consumption of goods and services. This course may be useful for an Economist seeking to understand the economic implications and applications of image recognition technology.
Business Analyst
Business Analyst
A Business Analyst helps businesses improve their performance by analyzing their systems and processes. This course may be helpful for a Business Analyst looking to leverage image recognition for business process improvement.
Operations Manager
Operations Manager
An Operations Manager is responsible for managing the day-to-day operations of an organization. This course may be useful for an Operations Manager exploring the use of image recognition to improve operational efficiency.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and executing marketing strategies. This course may be useful for a Marketing Manager seeking to understand how image recognition can enhance customer engagement or improve marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/hkgoig/implement
Reading list
We've selected 19 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
Implement Image Recognition with a Convolutional Neural Network.
Comprehensive guide to deep learning in Python, and covers a wide range of topics, including image recognition.
Provides a practical guide to deep learning for computer vision, with a focus on building real-world applications.
Provides a comprehensive overview of numerical optimization, which is used in computer vision for tasks such as image registration and object tracking.
Provides a mathematical and statistical approach to pattern recognition and machine learning, which are essential concepts for image recognition.
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Provides a comprehensive overview of matrix computations, which are essential for understanding the mathematics behind image recognition.
Provides a mathematical foundation for partial differential equations, which are used in computer vision for tasks such as image segmentation and object recognition.
Provides a comprehensive overview of convex optimization, which is used in computer vision for tasks such as image segmentation and object detection.
Save
Commonly used textbook at academic institutions for teaching deep learning.
Provides a comprehensive overview of image processing and computer vision, which are essential foundation topics for image recognition.
Provides a comprehensive overview of computer vision, with a focus on models, learning, and inference.
Save
Provides a comprehensive overview of computer vision, with a focus on algorithms and applications.
Commonly used textbook at academic institutions for teaching computer vision.
Save
Provides a comprehensive overview of the field of computer vision, with a focus on algorithms and applications.
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, and support vector machines. It valuable resource for anyone looking to learn more about this field.
Provides a more accessible introduction to statistical learning than The Elements of Statistical Learning. It valuable resource for anyone looking to learn more about this field.
Provides a practical guide to machine learning for non-programmers. It valuable resource for anyone looking to learn more about this field.
Provides a practical guide to machine learning using R. It valuable resource for anyone looking to learn more about this field.
Provides a practical guide to machine learning using Python. It valuable resource for anyone looking to learn more about this field.
Save
Provides a practical guide to machine learning using R. It valuable resource for anyone looking to learn more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hkgoig/implement
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Time
90 hours
Level
Intermediate
Via
Pluralsight
Instructor
Pratheerth Padman
Language
English
Good to know
Teaches the foundations and application of convolutional neural networks for image recognition
Suitable for beginners with little to no experience in image recognition or neural networks
Practical course that emphasizes hands-on implementation and experimentation
Covers essential image augmentation techniques to improve model performance
Provides a strong foundation for further exploration in image recognition and deep learning
May require additional resources or background knowledge for learners with no prior experience in Python or machine learning
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