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Amit Yadav
Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection. In Object Detection, we might have multiple objects in the input images, and an object detection model predicts the classes as well as bounding boxes for all of those objects. In Object Localization, we are working with the assumption that there is just one object in any given image, and our CNN model will classify and localize that object.
Read more
Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection. In Object Detection, we might have multiple objects in the input images, and an object detection model predicts the classes as well as bounding boxes for all of those objects. In Object Localization, we are working with the assumption that there is just one object in any given image, and our CNN model will classify and localize that object.
Read more
Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection. In Object Detection, we might have multiple objects in the input images, and an object detection model predicts the classes as well as bounding boxes for all of those objects. In Object Localization, we are working with the assumption that there is just one object in any given image, and our CNN model will classify and localize that object.
Please note that you will need prior programming experience in Python. You will also need familiarity with TensorFlow. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like Gradient Descent but want to understand how to use use TensorFlow to solve computer vision tasks like Object Localization.
Register for this course and see more details by visiting:
OpenCourser.com/course/k9blun/object
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Syllabus
Object Localization with TensorFlow
Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection. In Object Detection, we might have multiple objects in the input images, and an object detection model predicts the classes as well as bounding boxes for all of those objects. In Object Localization, we are working with the assumption that there is just one object in any given image, and our CNN model will classify and localize that object. Please note that you will need prior programming experience in Python. You will also need familiarity with TensorFlow. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like Gradient Descent but want to understand how to use use TensorFlow to solve computer vision tasks like Object Localization.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on a specific application of TensorFlow to solve real-world computer vision tasks
Introduces a simpler version of object localization, which makes it suitable for beginners in the field
Requires prior programming experience in Python and familiarity with TensorFlow
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Reviews summary
Solid intro to tensorflow object localization
Learners say that this course is a solid introduction to object localization with TensorFlow. According to students, it's helpful for beginners, but you'll need some prior knowledge in Python (OOP, TF, Keras, nn programming) to fully engage with the material. Reviews are largely positive, although one student notes the audio quality is not the best.
Course helps ease beginners into object localization.
"It is pretty good for ConvNets beginners"
"If you want to get basics to object localization. Give it a try."
Audio quality is not the best.
"audio quality is not the best"
Assumes some prior Python knowledge.
"you need to have prior knowlege in python(OOP, tf, keras, nn programming)"
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 Object Localization with TensorFlow with these
activities:
Review Foundations of Object Detection
Show steps
Review the foundational theories of object detection to strengthen your understanding of object localization.
Browse courses on
Computer Vision
Show steps
-
Read through lecture notes or textbooks on object detection.
-
Complete practice problems or quizzes on object detection principles.
-
Watch video tutorials on object detection algorithms.
Form a Study or Discussion Group
Show steps
Collaborate with peers to exchange knowledge, solve problems, and enhance your understanding.
Show steps
-
Identify fellow students or colleagues interested in object localization.
-
Establish regular meeting times and a communication platform.
-
Discuss course materials, share resources, and work on projects together.
Curate a Collection of Object Localization Resources
Show steps
Gather and organize valuable resources to support your learning journey and share insights with others.
Browse courses on
Learning Materials
Show steps
-
Search for articles, tutorials, videos, and other resources related to object localization.
-
Categorize and organize the resources based on topic or difficulty.
-
Create a digital or physical compilation for easy access and sharing.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Object Localization with TensorFlow
Show steps
Engage in hands-on exercises using TensorFlow to apply object localization techniques to real-world scenarios.
Browse courses on
Convolutional Neural Networks (CNNs)
Show steps
-
Follow along with the provided TensorFlow tutorial on object localization.
-
Experiment with different image datasets and CNN architectures.
-
Troubleshoot and debug your code to improve model performance.
Explore Advanced Object Localization Techniques
Show steps
Delve into more advanced object localization techniques to enhance your understanding and skills.
Browse courses on
Machine Learning
Show steps
-
Read research papers on state-of-the-art object detection algorithms.
-
Attend webinars or workshops on advanced object localization.
-
Implement advanced object localization techniques in your own projects.
Contribute to Open-Source Object Localization Projects
Show steps
Contribute to open-source projects to enhance your skills, learn from others, and give back to the community.
Browse courses on
Community Involvement
Show steps
-
Identify open-source object localization projects that align with your interests and skills.
-
Review the documentation and contribute code or other resources.
-
Collaborate with other contributors and the project maintainers.
Develop an Object Localization Model
Show steps
Create a practical object localization model to apply your knowledge and demonstrate your skills.
Browse courses on
Software Development
Show steps
-
Define the problem statement and gather the necessary data.
-
Design and implement a CNN architecture for object localization.
-
Train and evaluate the model using appropriate metrics.
-
Deploy the model for real-world applications.
Review Foundations of Object Detection
Show steps
Review the foundational theories of object detection to strengthen your understanding of object localization.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Read through lecture notes or textbooks on object detection.
- Complete practice problems or quizzes on object detection principles.
- Watch video tutorials on object detection algorithms.
Form a Study or Discussion Group
Show steps
Collaborate with peers to exchange knowledge, solve problems, and enhance your understanding.
Show steps
Show steps
Show steps
- Identify fellow students or colleagues interested in object localization.
- Establish regular meeting times and a communication platform.
- Discuss course materials, share resources, and work on projects together.
Curate a Collection of Object Localization Resources
Show steps
Gather and organize valuable resources to support your learning journey and share insights with others.
Browse courses on
Learning Materials
Show steps
Show steps
Show steps
- Search for articles, tutorials, videos, and other resources related to object localization.
- Categorize and organize the resources based on topic or difficulty.
- Create a digital or physical compilation for easy access and sharing.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Object Localization with TensorFlow
Show steps
Engage in hands-on exercises using TensorFlow to apply object localization techniques to real-world scenarios.
Browse courses on
Convolutional Neural Networks (CNNs)
Show steps
Show steps
Show steps
- Follow along with the provided TensorFlow tutorial on object localization.
- Experiment with different image datasets and CNN architectures.
- Troubleshoot and debug your code to improve model performance.
Explore Advanced Object Localization Techniques
Show steps
Delve into more advanced object localization techniques to enhance your understanding and skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read research papers on state-of-the-art object detection algorithms.
- Attend webinars or workshops on advanced object localization.
- Implement advanced object localization techniques in your own projects.
Contribute to Open-Source Object Localization Projects
Show steps
Contribute to open-source projects to enhance your skills, learn from others, and give back to the community.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify open-source object localization projects that align with your interests and skills.
- Review the documentation and contribute code or other resources.
- Collaborate with other contributors and the project maintainers.
Develop an Object Localization Model
Show steps
Create a practical object localization model to apply your knowledge and demonstrate your skills.
Browse courses on
Software Development
Show steps
Show steps
Show steps
- Define the problem statement and gather the necessary data.
- Design and implement a CNN architecture for object localization.
- Train and evaluate the model using appropriate metrics.
- Deploy the model for real-world applications.
Career center
Learners who complete Object Localization with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop, test, and maintain computer vision systems. These systems are used in a variety of applications, including image and video analysis, object recognition, and facial recognition. The Object Localization with TensorFlow course can help you build a foundation in computer vision by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of computer vision tasks, including those used in self-driving cars, medical imaging, and security systems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. These models are used in a variety of applications, including predictive analytics, natural language processing, and computer vision. The Object Localization with TensorFlow course can help you build a foundation in machine learning by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of machine learning tasks, including those used in healthcare, finance, and manufacturing.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They use a variety of statistical and machine learning techniques to analyze data and develop models. The Object Localization with TensorFlow course can help you build a foundation in data science by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of data science tasks, including those used in marketing, finance, and healthcare.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use a variety of programming languages and technologies to build software that meets the needs of users. The Object Localization with TensorFlow course can help you build a foundation in software engineering by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of software engineering tasks, including those used in web development, mobile development, and cloud computing.
Computer Vision Researcher
Computer Vision Researcher
Computer Vision Researchers develop new computer vision algorithms and techniques. They use a variety of mathematical and statistical techniques to improve the accuracy and efficiency of computer vision systems. The Object Localization with TensorFlow course can help you build a foundation in computer vision research by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of computer vision research topics, including those used in object recognition, image segmentation, and motion analysis.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. They use a variety of mechanical, electrical, and computer engineering principles to create robots that can perform a variety of tasks. The Object Localization with TensorFlow course can help you build a foundation in robotics engineering by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of robotics engineering tasks, including those used in manufacturing, healthcare, and space exploration.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, develop, and test autonomous vehicles. They use a variety of sensors and algorithms to create vehicles that can safely navigate without human input. The Object Localization with TensorFlow course can help you build a foundation in autonomous vehicle engineering by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of autonomous vehicle engineering tasks, including those used in self-driving cars, trucks, and drones.
Medical Imaging Analyst
Medical Imaging Analyst
Medical Imaging Analysts use medical imaging data to diagnose and treat diseases. They use a variety of image processing and analysis techniques to identify abnormalities in medical images. The Object Localization with TensorFlow course can help you build a foundation in medical imaging analysis by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of medical imaging analysis tasks, including those used in cancer detection, radiology, and pathology.
Security Analyst
Security Analyst
Security Analysts protect computer systems and networks from unauthorized access and attack. They use a variety of security measures to identify and prevent security breaches. The Object Localization with TensorFlow course can help you build a foundation in security analysis by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set can be applied to a variety of security analysis tasks, including those used in intrusion detection, malware analysis, and forensics.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use a variety of statistical and data mining techniques to extract meaningful insights from data. The Object Localization with TensorFlow course may help you build a foundation in data analysis by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set may be applied to a variety of data analysis tasks, including those used in marketing, finance, and healthcare.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems. They use a variety of analytical techniques to understand business processes and develop solutions. The Object Localization with TensorFlow course may help you build a foundation in business analysis by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set may be applied to a variety of business analysis tasks, including those used in process improvement, product development, and customer relationship management.
Product Manager
Product Manager
Product Managers develop and manage products. They work with engineers, designers, and marketers to create products that meet the needs of users. The Object Localization with TensorFlow course may help you build a foundation in product management by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set may be applied to a variety of product management tasks, including those used in product planning, development, and marketing.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with stakeholders to define project goals and objectives and develop plans to achieve them. The Object Localization with TensorFlow course may help you build a foundation in project management by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set may be applied to a variety of project management tasks, including those used in planning, scheduling, and budgeting.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. They use their expertise in a particular field to help clients solve problems and improve performance. The Object Localization with TensorFlow course may help you build a foundation in consulting by teaching you how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set may be applied to a variety of consulting tasks, including those used in strategic planning, organizational development, and financial analysis.
Teacher
Teacher
Teachers educate students in a variety of subjects. They develop lesson plans, teach classes, and assess student learning. The Object Localization with TensorFlow course may help you build a foundation in teaching by providing you with an understanding of how to use TensorFlow to create and train a convolutional neural network for object localization. This knowledge and skill set may be applied to a variety of teaching tasks, including those used in lesson planning, classroom management, and assessment.
For more career information including salaries, visit:
OpenCourser.com/course/k9blun/object
Reading list
We've selected 11 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
Object Localization with TensorFlow.
Covers the fundamentals of deep learning, including convolutional neural networks, recurrent neural networks, and deep reinforcement learning. This book will help you learn how to train your own convolutional neural network for object localization.
Save
Provides a comprehensive overview of computer vision algorithms and techniques, including image processing, feature extraction, and object recognition. This book will help you understand the underlying principles of object localization.
Provides a practical introduction to TensorFlow, the open-source machine learning library from Google. This book will help you learn how to use TensorFlow to build and train your own deep learning models for object localization.
This textbook provides a deep dive into using deep learning for computer vision tasks, including image classification, object detection, and segmentation. will help you learn how to build, train, and evaluate your own deep learning models for object localization.
Provides a comprehensive overview of the mathematical foundations of machine learning, including linear algebra, calculus, and probability theory. This book will help you understand the mathematical concepts underlying object localization.
Save
This textbook provides an introduction to deep learning with R. will help you learn how to use R to build and train your own deep learning models.
Save
Provides a comprehensive overview of artificial intelligence. This book will help you understand the fundamental concepts of AI and how they apply to object localization.
Provides an introduction to natural language processing with Python. This book will help you learn how to use Python to perform natural language processing tasks, such as text classification, sentiment analysis, and named entity recognition.
Provides a comprehensive overview of machine learning with PyTorch and Scikit-Learn. This book will help you learn how to use PyTorch and Scikit-Learn to build and train your own machine learning models.
Provides a hands-on introduction to data science. This book will help you learn how to use Python to perform data analysis and machine learning tasks.
Provides a visual introduction to deep learning. This book will help you understand the fundamental concepts of deep learning and how they apply to object localization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k9blun/object
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Focuses on a specific application of TensorFlow to solve real-world computer vision tasks
Introduces a simpler version of object localization, which makes it suitable for beginners in the field
Requires prior programming experience in Python and familiarity with TensorFlow
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