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Jon Flanders
TensorFlow is popular a library for implementing a range of deep learning solutions but is especially useful for solutions that deal with images. This course will teach you the basics of how to use TensorFlow to implement the most typical scenarios.
Read more
TensorFlow is popular a library for implementing a range of deep learning solutions but is especially useful for solutions that deal with images. This course will teach you the basics of how to use TensorFlow to implement the most typical scenarios.
Read more
TensorFlow is popular a library for implementing a range of deep learning solutions but is especially useful for solutions that deal with images. This course will teach you the basics of how to use TensorFlow to implement the most typical scenarios.
Running images through deep learning models is potentially the most typical scenario in which deep learning is used today. In this course, Implementing Image Recognition Systems with TensorFlow 1, you will learn the basics of how to implement a solution for the most typical deep learning imaging scenarios. First, you will learn how to pick a TensorFlow model architecture if you can implement your solution with pre-existing, pre-trained models. Next, you will learn how to extend such models using your own training images by taking advantage of transfer learning. Finally, you will see how to use more advanced solutions to do more advanced processing on images, like segmentation, and even learn how to implement a facial recognition solution. When you are finished with this course, you will have the skills and knowledge of TensorFlow and imaging in order to implement your own solutions successfully.
Register for this course and see more details by visiting:
OpenCourser.com/course/5plr15/implementing
What's inside
Syllabus
Course Overview
Introduction
Picking and Using a Model
Transfer Learning
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Syllabus
Course Overview
Introduction
Picking and Using a Model
Transfer Learning
Read more
Localization and Segmentation
Face Recognition
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops core foundational skills in image processing, computer vision, deep learning, and data analysis, making it useful for careers in technology, data science, or related fields
Offers hands-on labs and interactive materials, fostering the application of concepts in a practical setting
Provides a deep dive into image recognition systems, catering to learners interested in specializing in this domain
Explores advanced solutions for image processing, including segmentation and facial recognition, appealing to learners seeking a comprehensive understanding of image analysis techniques
Taught by Jon Flanders, an experienced instructor recognized for his expertise in deep learning and image processing
Presents a strong foundation in TensorFlow, a widely used library for implementing deep learning solutions, particularly in image processing
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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 Implementing Image Recognition Systems with TensorFlow 1 with these
activities:
Deep Learning with TensorFlow 2 and Keras
Show steps
Gain theoretical understanding of deep learning and TensorFlow from a comprehensive resource.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
-
Read through the book's introduction and key concepts.
-
Study the chapters on TensorFlow basics, data preprocessing, and model training.
Linear Algebra Review
Show steps
Strengthen the foundational knowledge of linear algebra concepts necessary for TensorFlow.
Browse courses on
Mathematics
Show steps
-
Review the concepts of vectors, matrices, and linear transformations.
-
Practice solving systems of linear equations.
-
Understand the basics of eigenvalues and eigenvectors.
Image Preprocessing Exercise
Show steps
Practice different image preprocessing techniques for use within TensorFlow models.
Browse courses on
Image Processing
Show steps
-
Load a set of images into TensorFlow.
-
Apply different preprocessing operations, such as resizing, cropping, and normalization.
-
Save the preprocessed images to a new directory.
One other activity
Expand to see all activities and additional details
Show all four activities
Object Detection Project
Show steps
Develop a practical understanding of object detection by building a project that demonstrates its application.
Browse courses on
Image Processing
Show steps
-
Choose an object detection dataset.
-
Train an object detection model using TensorFlow.
-
Deploy the model and evaluate its performance.
Deep Learning with TensorFlow 2 and Keras
Show steps
Gain theoretical understanding of deep learning and TensorFlow from a comprehensive resource.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
Show steps
Show steps
- Read through the book's introduction and key concepts.
- Study the chapters on TensorFlow basics, data preprocessing, and model training.
Linear Algebra Review
Show steps
Strengthen the foundational knowledge of linear algebra concepts necessary for TensorFlow.
Browse courses on
Mathematics
Show steps
Show steps
Show steps
- Review the concepts of vectors, matrices, and linear transformations.
- Practice solving systems of linear equations.
- Understand the basics of eigenvalues and eigenvectors.
Image Preprocessing Exercise
Show steps
Practice different image preprocessing techniques for use within TensorFlow models.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Load a set of images into TensorFlow.
- Apply different preprocessing operations, such as resizing, cropping, and normalization.
- Save the preprocessed images to a new directory.
One other activity
Expand to see all activities and additional details
Show all four activities
Object Detection Project
Show steps
Develop a practical understanding of object detection by building a project that demonstrates its application.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Choose an object detection dataset.
- Train an object detection model using TensorFlow.
- Deploy the model and evaluate its performance.
Career center
Learners who complete Implementing Image Recognition Systems with TensorFlow 1 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning models, which are used to solve a variety of problems in a range of industries. This course on Implementing Image Recognition Systems with TensorFlow 1 provides a solid foundation in the basics of TensorFlow, one of the most popular libraries for implementing deep learning solutions, with a focus on image recognition. This knowledge is essential for Machine Learning Engineers who want to work on image-related projects.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They use a variety of techniques, including machine learning, to extract insights from data. This course on Implementing Image Recognition Systems with TensorFlow 1 provides a solid foundation in the basics of TensorFlow, one of the most popular libraries for implementing deep learning solutions. This knowledge is essential for Data Scientists who want to work on image-related projects.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems, which are used to give computers the ability to see and understand images. This course on Implementing Image Recognition Systems with TensorFlow 1 provides a solid foundation in the basics of TensorFlow, one of the most popular libraries for implementing deep learning solutions, with a focus on image recognition. This knowledge is essential for Computer Vision Engineers who want to work on image-related projects.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course on Implementing Image Recognition Systems with TensorFlow 1 provides a solid foundation in the basics of TensorFlow, one of the most popular libraries for implementing deep learning solutions. This knowledge is essential for Software Engineers who want to work on image-related projects.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers develop and implement deep learning models, which are used to solve a variety of problems in a range of industries. This course on Implementing Image Recognition Systems with TensorFlow 1 provides a solid foundation in the basics of TensorFlow, one of the most popular libraries for implementing deep learning solutions, with a focus on image recognition. This knowledge is essential for Deep Learning Engineers who want to work on image-related projects.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems, which are used to solve a variety of problems in a range of industries. This course on Implementing Image Recognition Systems with TensorFlow 1 provides a solid foundation in the basics of TensorFlow, one of the most popular libraries for implementing deep learning solutions. This knowledge is essential for Artificial Intelligence Engineers who want to work on image-related projects.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. This course on Implementing Image Recognition Systems with TensorFlow 1 provides a solid foundation in the basics of TensorFlow, one of the most popular libraries for implementing deep learning solutions. This knowledge may be useful for Research Scientists who want to work on image-related projects.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to ensure that products meet the needs of customers. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Product Managers who want to work on image-related products.
Business Analyst
Business Analyst
Business Analysts analyze business processes and make recommendations for improvement. They work with stakeholders to identify and solve problems. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Business Analysts who want to work on image-related projects.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to ensure that projects are completed on time, within budget, and to the required quality. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Project Managers who want to work on image-related projects.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and other technical staff to ensure that documentation is accurate and easy to understand. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Technical Writers who want to write documentation for image-related products.
Sales Engineer
Sales Engineer
Sales Engineers work with customers to help them understand and purchase technical products. They work with engineers and other technical staff to ensure that customers get the right products for their needs. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Sales Engineers who want to work with customers on image-related products.
Marketing Manager
Marketing Manager
Marketing Managers develop and implement marketing campaigns. They work with customers to create awareness of products and services. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Marketing Managers who want to develop campaigns for image-related products.
Customer Success Manager
Customer Success Manager
Customer Success Managers work with customers to ensure that they are successful with their products. They work with customers to identify and solve problems. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Customer Success Managers who want to work with customers on image-related products.
Consultant
Consultant
Consultants provide advice and guidance to businesses. They work with clients to identify and solve problems. This course on Implementing Image Recognition Systems with TensorFlow 1 may be useful for Consultants who want to work on image-related projects.
For more career information including salaries, visit:
OpenCourser.com/course/5plr15/implementing
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
Implementing Image Recognition Systems with TensorFlow 1.
Is comprised of various advanced and intermediate-level recipes that teach you how to apply TensorFlow to solve deep learning problems. Use this as a reference or for supplemental reading to expand your knowledge of how to use TensorFlow.
Is for intermediate-level learners who want to expand their use of TensorFlow and Keras for deep learning. It provides a solid foundation in deep learning with many helpful examples that can supplement this course.
Offers a comprehensive overview of deep learning concepts and techniques using Python. It provides a solid foundation for those interested in building deep learning models.
Presents a probabilistic approach to machine learning. It provides a theoretical foundation for understanding machine learning algorithms and their applications.
Provides a comprehensive treatment of pattern recognition and machine learning techniques. It offers a deep dive into the theoretical foundations and practical applications of these technologies.
Save
Is an advanced and comprehensive reference on deep learning. It provides a thorough exploration of the latest research and advancements in the field.
Save
Provides a comprehensive overview of computer vision algorithms and techniques. It covers a wide range of topics, including image processing, object detection, and recognition.
Classic reference on digital image processing. It provides a comprehensive treatment of the field, covering topics such as image enhancement, segmentation, and analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5plr15/implementing
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Time
117 hours
Level
Intermediate
Via
Pluralsight
Instructor
Jon Flanders
Language
English
This course belongs to a
collection
called
Building Machine Learning Solutions with Tensorflow. It's comprised of 11 courses:
- TensorFlow 1: Getting Started
- Understanding the Foundations of TensorFlow
- Building Regression Models Using TensorFlow 1
- Building Classification Models with TensorFlow
- Building Unsupervised Learning Models with TensorFlow
- Debugging and Monitoring TensorFlow Programs
- Deploying TensorFlow Models to AWS, Azure, and the GCP
- Language Modeling with Recurrent Neural Networks in TensorFlow
- Implementing Image Recognition Systems with TensorFlow 1 (viewing)
- Implementing Predictive Analytics with TensorFlow
- Sentiment Analysis with Recurrent Neural Networks in TensorFlow
Good to know
Develops core foundational skills in image processing, computer vision, deep learning, and data analysis, making it useful for careers in technology, data science, or related fields
Offers hands-on labs and interactive materials, fostering the application of concepts in a practical setting
Provides a deep dive into image recognition systems, catering to learners interested in specializing in this domain
Explores advanced solutions for image processing, including segmentation and facial recognition, appealing to learners seeking a comprehensive understanding of image analysis techniques
Taught by Jon Flanders, an experienced instructor recognized for his expertise in deep learning and image processing
Presents a strong foundation in TensorFlow, a widely used library for implementing deep learning solutions, particularly in image processing
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