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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
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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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Save Implement Image Recognition with a Convolutional Neural Network to your list so you can find it easily later:
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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 Implement Image Recognition with a Convolutional Neural Network with these
activities:
Review CNN
Show steps
Refresh your knowledge of CNNs before starting the course to enhance your comprehension and ability to apply the concepts effectively.
Browse courses on
Convolutional Neural Network
Show steps
-
Review the concept of convolutional operations.
-
Recall the working principle of pooling layers.
-
Go through the different types of activation functions.
Deep Learning with Python
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques, including convolutional neural networks, which will enhance your understanding of the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the chapters on convolutional neural networks.
-
Work through the exercises and examples provided in the book.
-
Apply the concepts to your own image recognition project.
Image Recognition Exercise
Show steps
Practice identifying and classifying images to improve your ability to recognize patterns and extract meaningful features.
Browse courses on
Image Recognition
Show steps
-
Collect a dataset of images from various categories.
-
Manually label the images with their corresponding categories.
-
Train a simple CNN model on the labeled dataset.
-
Evaluate the performance of the model on unseen images.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Tutorial for Image Recognition
Show steps
This tutorial will provide you with hands-on experience in using TensorFlow to build and train image recognition models, enhancing your practical skills.
Browse courses on
TensorFlow
Show steps
-
Follow the steps in the TensorFlow tutorial.
-
Experiment with different hyperparameters and architectures.
-
Apply the model to your own image recognition task.
Explore Image Augmentation Techniques
Show steps
Learn about different image augmentation techniques to enhance your data and improve model performance.
Browse courses on
Image Augmentation
Show steps
-
Read articles and watch videos on image augmentation.
-
Implement basic augmentation techniques in Python or R.
-
Apply augmentation to your own image dataset.
Develop a Poster on Image Recognition
Show steps
Creating a poster will help you synthesize and communicate your understanding of image recognition concepts in a concise and visually appealing manner.
Browse courses on
Image Recognition
Show steps
-
Gather key information and concepts related to image recognition.
-
Design a layout and organize the content effectively.
-
Use visuals, such as diagrams and examples, to illustrate concepts.
-
Present your poster in a clear and engaging way.
Build and Test a Custom CNN Model
Show steps
By building and testing your own CNN model, you will gain practical experience in applying the concepts and techniques learned in the course.
Browse courses on
Model Building
Show steps
-
Define the architecture of your CNN model.
-
Train the model on your own dataset or a publicly available dataset.
-
Evaluate the performance of the model using appropriate metrics.
-
Fine-tune the model to improve its accuracy and efficiency.
Contribute to Open Source CNN Projects
Show steps
Contributing to open source projects allows you to collaborate with others, learn from experienced developers, and make a meaningful impact on the field of image recognition.
Browse courses on
Open Source
Show steps
-
Identify open source CNN projects that align with your interests.
-
Review the project's documentation and codebase.
-
Identify areas where you can contribute.
-
Submit your contributions to the project's repository.
Review CNN
Show steps
Refresh your knowledge of CNNs before starting the course to enhance your comprehension and ability to apply the concepts effectively.
Browse courses on
Convolutional Neural Network
Show steps
Show steps
Show steps
- Review the concept of convolutional operations.
- Recall the working principle of pooling layers.
- Go through the different types of activation functions.
Deep Learning with Python
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques, including convolutional neural networks, which will enhance your understanding of the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on convolutional neural networks.
- Work through the exercises and examples provided in the book.
- Apply the concepts to your own image recognition project.
Image Recognition Exercise
Show steps
Practice identifying and classifying images to improve your ability to recognize patterns and extract meaningful features.
Browse courses on
Image Recognition
Show steps
Show steps
Show steps
- Collect a dataset of images from various categories.
- Manually label the images with their corresponding categories.
- Train a simple CNN model on the labeled dataset.
- Evaluate the performance of the model on unseen images.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Tutorial for Image Recognition
Show steps
This tutorial will provide you with hands-on experience in using TensorFlow to build and train image recognition models, enhancing your practical skills.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow the steps in the TensorFlow tutorial.
- Experiment with different hyperparameters and architectures.
- Apply the model to your own image recognition task.
Explore Image Augmentation Techniques
Show steps
Learn about different image augmentation techniques to enhance your data and improve model performance.
Browse courses on
Image Augmentation
Show steps
Show steps
Show steps
- Read articles and watch videos on image augmentation.
- Implement basic augmentation techniques in Python or R.
- Apply augmentation to your own image dataset.
Develop a Poster on Image Recognition
Show steps
Creating a poster will help you synthesize and communicate your understanding of image recognition concepts in a concise and visually appealing manner.
Browse courses on
Image Recognition
Show steps
Show steps
Show steps
- Gather key information and concepts related to image recognition.
- Design a layout and organize the content effectively.
- Use visuals, such as diagrams and examples, to illustrate concepts.
- Present your poster in a clear and engaging way.
Build and Test a Custom CNN Model
Show steps
By building and testing your own CNN model, you will gain practical experience in applying the concepts and techniques learned in the course.
Browse courses on
Model Building
Show steps
Show steps
Show steps
- Define the architecture of your CNN model.
- Train the model on your own dataset or a publicly available dataset.
- Evaluate the performance of the model using appropriate metrics.
- Fine-tune the model to improve its accuracy and efficiency.
Contribute to Open Source CNN Projects
Show steps
Contributing to open source projects allows you to collaborate with others, learn from experienced developers, and make a meaningful impact on the field of image recognition.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open source CNN projects that align with your interests.
- Review the project's documentation and codebase.
- Identify areas where you can contribute.
- Submit your contributions to the project's repository.
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.
Save
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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