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Alex Schultz
This course will teach you the fundamental concepts required to understand how convolutional neural networks work.
Read more
This course will teach you the fundamental concepts required to understand how convolutional neural networks work.
Read more
This course will teach you the fundamental concepts required to understand how convolutional neural networks work.
Convolutional Neural Networks have given computers a near human-level ability to make sense of image data. In this course, Literacy Essentials: Core Concepts Convolutional Neural Network, you’ll develop a fundamental understanding of how convolutional neural networks operate. First, you’ll explore the layers that make up convolutional neural networks. Next, you’ll discover how those layers, when trained on a dataset, work together to identify classes in an image. Finally, you’ll learn how to use existing convolutional neural networks with minimal effort. When you’re finished with this course, you’ll have the skills and knowledge required to understand how CNNs (Convolutional Neural Network) work so you can start exploring more advanced Convolutional Neural Networks needed to add understanding of images to your applications.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Course Overview
Introduction
Exploring Convolutional Neural Network Architecture
Next Steps
Read more
Syllabus
Course Overview
Introduction
Exploring Convolutional Neural Network Architecture
Next Steps
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with little to no experience with convolutional neural networks
Helps learners understand how convolutional neural networks identify classes in images
Teaches learners how to use existing convolutional neural networks with minimal effort
Provides a solid foundation for learners interested in exploring more advanced Convolutional Neural Networks
Save this course
Save Literacy Essentials : Core Concepts 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 Literacy Essentials : Core Concepts Convolutional Neural Network with these
activities:
Read 'Deep Learning with Python'
Show steps
Concepts of convolutional neural networks are covered in-depth, providing additional insights.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the relevant chapters on convolutional neural networks.
-
Focus on understanding the mathematical foundations and practical implementation details of CNNs.
Review Matrix Operations
Show steps
Strengthens the foundation for understanding the mathematical operations used in CNNs (e.g., convolutions, pooling).
Browse courses on
Matrix Operations
Show steps
-
Revisit basic matrix operations such as addition, subtraction, multiplication, and transposition.
-
Practice performing matrix operations to manipulate image data.
Explore Keras CNN Tutorials
Show steps
Practical application and understanding of CNN architecture and implementation.
Show steps
-
Follow tutorials on building and training CNNs using the Keras API.
-
Experiment with different CNN architectures, such as LeNet-5 and VGGNet.
-
Analyze the performance of pre-trained CNN models on image classification tasks.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice convolutional layer operations
Show steps
Reinforce the understanding of convolutional layer operations and their effects on image data.
Show steps
-
Implement a simple convolutional layer in Python or another programming language.
-
Experiment with varying the kernel size, stride, and padding to observe their impact on output dimensions.
-
Analyze the resulting feature maps to visualize how convolutional layers extract features from images.
Attend a CNN Workshop
Show steps
Practical hands-on application in a structured learning environment.
Show steps
-
Participate in hands-on exercises and demonstrations to build and train CNNs.
-
Interact with experts and ask questions to enhance understanding.
Visualize CNN Architecture
Show steps
Develop a deeper understanding of the structure and operation of CNNs by visualizing their architecture.
Show steps
-
Create a visual representation of a CNN architecture, such as a diagram or flow chart.
-
Annotate the diagram with descriptions of each layer's function and purpose.
-
Use the visualization to explain the sequential processing of images through the CNN.
Build a Simple Image Classifier
Show steps
Practical application of concepts and skills learned in the course through a tangible project.
Show steps
-
Collect and prepare a dataset of images for classification.
-
Design and implement a CNN architecture for the classification task.
-
Train and evaluate the CNN model.
Read 'Deep Learning with Python'
Show steps
Concepts of convolutional neural networks are covered in-depth, providing additional insights.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters on convolutional neural networks.
- Focus on understanding the mathematical foundations and practical implementation details of CNNs.
Review Matrix Operations
Show steps
Strengthens the foundation for understanding the mathematical operations used in CNNs (e.g., convolutions, pooling).
Browse courses on
Matrix Operations
Show steps
Show steps
Show steps
- Revisit basic matrix operations such as addition, subtraction, multiplication, and transposition.
- Practice performing matrix operations to manipulate image data.
Explore Keras CNN Tutorials
Show steps
Practical application and understanding of CNN architecture and implementation.
Show steps
Show steps
Show steps
- Follow tutorials on building and training CNNs using the Keras API.
- Experiment with different CNN architectures, such as LeNet-5 and VGGNet.
- Analyze the performance of pre-trained CNN models on image classification tasks.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice convolutional layer operations
Show steps
Reinforce the understanding of convolutional layer operations and their effects on image data.
Show steps
Show steps
Show steps
- Implement a simple convolutional layer in Python or another programming language.
- Experiment with varying the kernel size, stride, and padding to observe their impact on output dimensions.
- Analyze the resulting feature maps to visualize how convolutional layers extract features from images.
Attend a CNN Workshop
Show steps
Practical hands-on application in a structured learning environment.
Show steps
Show steps
Show steps
- Participate in hands-on exercises and demonstrations to build and train CNNs.
- Interact with experts and ask questions to enhance understanding.
Visualize CNN Architecture
Show steps
Develop a deeper understanding of the structure and operation of CNNs by visualizing their architecture.
Show steps
Show steps
Show steps
- Create a visual representation of a CNN architecture, such as a diagram or flow chart.
- Annotate the diagram with descriptions of each layer's function and purpose.
- Use the visualization to explain the sequential processing of images through the CNN.
Build a Simple Image Classifier
Show steps
Practical application of concepts and skills learned in the course through a tangible project.
Show steps
Show steps
Show steps
- Collect and prepare a dataset of images for classification.
- Design and implement a CNN architecture for the classification task.
- Train and evaluate the CNN model.
Career center
Learners who complete Literacy Essentials : Core Concepts Convolutional Neural Network will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze data to extract meaningful insights and patterns. They use their findings to solve business problems and make informed decisions. This course provides a strong foundation in the fundamental concepts of Convolutional Neural Networks, which are essential for Data Scientists who work with image data. By understanding how CNNs work, Data Scientists can develop more effective models for image classification, object detection, and other tasks.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use their knowledge of machine learning algorithms and techniques to solve real-world problems. This course provides a comprehensive introduction to the concepts and techniques used in Convolutional Neural Networks, which are a powerful tool for Machine Learning Engineers who work with image data. By completing this course, Machine Learning Engineers can gain the skills and knowledge they need to develop and deploy more effective CNN models.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems that can interpret and understand images and videos. They use their knowledge of computer vision algorithms and techniques to solve real-world problems, such as object detection, facial recognition, and medical imaging. This course provides a solid foundation in the fundamental concepts of Convolutional Neural Networks, which are a key technology for Computer Vision Engineers. By understanding how CNNs work, Computer Vision Engineers can develop more effective and accurate computer vision systems.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design and develop deep learning models. They use their knowledge of deep learning algorithms and techniques to solve real-world problems, such as natural language processing, image recognition, and speech recognition. This course provides a comprehensive introduction to the concepts and techniques used in Convolutional Neural Networks, which are a powerful tool for Deep Learning Engineers who work with image data. By completing this course, Deep Learning Engineers can gain the skills and knowledge they need to develop and deploy more effective CNN models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. They use their knowledge of artificial intelligence algorithms and techniques to solve real-world problems, such as natural language processing, image recognition, and robotics. This course provides a solid foundation in the fundamental concepts of Convolutional Neural Networks, which are a key technology for Artificial Intelligence Engineers. By understanding how CNNs work, Artificial Intelligence Engineers can develop more effective and intelligent AI systems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of software engineering principles and techniques to solve real-world problems. This course provides a strong foundation in the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Software Engineers who work on image-based applications. By understanding how CNNs work, Software Engineers can develop more effective and efficient software systems.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights and patterns. They use their findings to solve business problems and make informed decisions. This course provides a strong foundation in the fundamental concepts of Convolutional Neural Networks, which are essential for Data Analysts who work with image data. By understanding how CNNs work, Data Analysts can develop more effective models for image classification, object detection, and other tasks.
Research Scientist
Research Scientist
Research Scientists conduct research to advance scientific knowledge and develop new technologies. They use their knowledge of scientific principles and techniques to solve complex problems. This course provides a solid foundation in the fundamental concepts of Convolutional Neural Networks, which are a powerful tool for Research Scientists who work with image data. By understanding how CNNs work, Research Scientists can develop more effective and innovative research methods.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and services. They work with engineers, designers, and marketers to ensure that products meet the needs of customers. This course provides a valuable introduction to the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Product Managers who work on image-based products. By understanding how CNNs work, Product Managers can make more informed decisions about the development and launch of new products and services.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. They use their knowledge of business analysis techniques to develop solutions that meet the needs of the business. This course provides a strong foundation in the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Business Analysts who work on image-based data. By understanding how CNNs work, Business Analysts can develop more effective solutions that improve business performance.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. They work with designers, copywriters, and other marketing professionals to create and deliver marketing materials that reach the target audience. This course provides a valuable introduction to the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Marketing Managers who work on image-based marketing campaigns. By understanding how CNNs work, Marketing Managers can make more informed decisions about the development and execution of marketing campaigns.
Sales Manager
Sales Manager
Sales Managers are responsible for the development and execution of sales strategies. They work with sales teams to identify and close deals. This course provides a valuable introduction to the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Sales Managers who work with image-based products and services. By understanding how CNNs work, Sales Managers can make more informed decisions about the development and execution of sales strategies.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. They work with customers to identify and resolve issues, and to provide training and support. This course provides a valuable introduction to the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Customer Success Managers who work with image-based products and services. By understanding how CNNs work, Customer Success Managers can make more informed decisions about how to support customers and resolve issues.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and delivery of projects. They work with teams to identify and achieve project goals. This course provides a valuable introduction to the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Project Managers who work on image-based projects. By understanding how CNNs work, Project Managers can make more informed decisions about the planning, execution, and delivery of projects.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts are responsible for testing and evaluating software products to ensure that they meet quality standards. They work with developers and testers to identify and fix bugs. This course provides a valuable introduction to the fundamental concepts of Convolutional Neural Networks, which are becoming increasingly important for Quality Assurance Analysts who work on image-based products. By understanding how CNNs work, Quality Assurance Analysts can make more informed decisions about testing and evaluating software products.
For more career information including salaries, visit:
OpenCourser.com/course/r6ao9y/literacy
Reading list
We've selected eight 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
Literacy Essentials : Core Concepts Convolutional Neural Network.
Save
Provides a comprehensive overview of deep learning, covering the latest advances in the field. It valuable reference for anyone who wants to learn more about deep learning.
Save
Provides a comprehensive overview of computer vision, covering the algorithms and applications of computer vision. It valuable resource for anyone who wants to learn more about computer vision.
Provides a comprehensive overview of pattern recognition and machine learning, covering the algorithms and applications of pattern recognition and machine learning. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning.
Provides a hands-on introduction to machine learning, covering the algorithms and applications of machine learning. It valuable resource for anyone who wants to learn more about machine learning.
Provides a gentle introduction to machine learning, covering the algorithms and applications of machine learning. It valuable resource for anyone who wants to learn more about machine learning.
Provides a practical introduction to deep learning, covering the algorithms and applications of deep learning. It valuable resource for anyone who wants to learn more about deep learning.
Provides a practical introduction to machine learning, covering the algorithms and applications of machine learning. It valuable resource for anyone who wants to learn more about machine learning.
Provides a practical introduction to machine learning, covering the algorithms and applications of machine learning. It valuable resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/r6ao9y/literacy
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Time
66 hours
Level
Introductory
Via
Pluralsight
Instructor
Alex Schultz
Language
English
Good to know
Suitable for learners with little to no experience with convolutional neural networks
Helps learners understand how convolutional neural networks identify classes in images
Teaches learners how to use existing convolutional neural networks with minimal effort
Provides a solid foundation for learners interested in exploring more advanced Convolutional Neural Networks
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