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Mo Rebaie
This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners...
Read more
This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners...
Read more
This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
In this 2-hour long project-based course, you will learn In this project, you will learn practically how to build an object recognition model in computer vision with real-world applications, and you will create your own object recognition algorithm with TensorFlow using real data, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned the fundamentals of object recognition and created a deep learning model with TensorFlow on a real-world dataset.
This class is for learners who want to learn how to work with convolutional neural networks and use Python for solving object recognition tasks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and training convolutional neural networks and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.
Register for this course and see more details by visiting:
OpenCourser.com/course/3oeplv/tensorflow
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possible dealbreakers
Suitable for beginners who seek a practical project with TensorFlow
Develops skills in Python and object recognition tasks, valuable for data scientists
Provides bonus deep learning exercise with TensorFlow for advanced learners
May require learners to have basic understanding of deep learning
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Reviews summary
Practical tensorflow cnns
This two-hour project-based course focuses on the practical application of convolutional neural networks and object recognition models using TensorFlow. Suitable for learners with a basic understanding of deep learning who seek to enhance their TensorFlow skills, this course provides a hands-on approach to building object recognition algorithms with real-world data.
Project can enhance portfolios and career goals
"...helps them in fulfilling their career goals by adding this project to their portfolios."
Emphasis on TensorFlow for object recognition tasks
"...how to work with convolutional neural networks and use Python for solving object recognition tasks with TensorFlow..."
Practical, project-based approach
"...you will learn practically how to build..."
"...create your own object recognition algorithm..."
Limited code accessibility and unhelpful content
"...can't download code; simple code-fill not coding the project."
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 TensorFlow for CNNs: Object Recognition with these
activities:
Read "Deep Learning with TensorFlow 2 and Keras"
Show steps
Reading "Deep Learning with TensorFlow 2 and Keras" will provide you with a comprehensive overview of deep learning and TensorFlow, which will help you understand the concepts covered in the course and apply them to your own projects.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
-
Read the book
-
Take notes
-
Complete the exercises
Follow a tutorial on object recognition with TensorFlow
Show steps
Following a tutorial on object recognition with TensorFlow will give you a step-by-step guide to building a model, which can help you learn the process and apply it to your own projects.
Show steps
-
Find a tutorial on object recognition with TensorFlow
-
Follow the steps in the tutorial
-
Experiment with the code and try to understand how it works
Create a compilation of resources on object recognition
Show steps
Creating a compilation of resources on object recognition will help you organize and synthesize the information you learn in the course and make it easier to access and review later.
Show steps
-
Gather resources on object recognition
-
Organize the resources into a logical structure
-
Create a document or website to share the compilation
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice building simple CNN models
Show steps
Practicing building simple CNN models will help you develop a deeper understanding of how they work and how to use them effectively.
Show steps
-
Find a dataset of images
-
Load the dataset into TensorFlow
-
Create a CNN model
-
Train the model
-
Evaluate the model
Attend a workshop on TensorFlow
Show steps
Attending a workshop on TensorFlow will give you hands-on experience with the framework and help you learn how to use it effectively.
Show steps
-
Find a workshop on TensorFlow
-
Attend the workshop
-
Complete the exercises
Attend a meetup or conference on deep learning
Show steps
Attending a meetup or conference on deep learning will allow you to connect with other people who are interested in the field and learn about their work.
Show steps
-
Find a meetup or conference on deep learning
-
Attend the event
-
Network with other attendees
Volunteer to help with a project related to deep learning
Show steps
Volunteering to help with a project related to deep learning will give you practical experience and allow you to contribute to the community.
Show steps
-
Find a project related to deep learning
-
Contact the project organizers
-
Volunteer your time
Create a model in TensorFlow
Show steps
Creating a model in TensorFlow will allow you to apply the concepts you learn in the course and deepen your understanding of object recognition.
Show steps
-
Gather the necessary data
-
Prepare the data for training
-
Build the model architecture
-
Train the model
-
Evaluate the model
Start a project to build an object recognition system
Show steps
Starting a project to build an object recognition system will allow you to apply the concepts you learn in the course and develop a real-world application.
Show steps
-
Define the scope of your project
-
Gather the necessary resources
-
Build the system
-
Test the system
-
Deploy the system
Read "Deep Learning with TensorFlow 2 and Keras"
Show steps
Reading "Deep Learning with TensorFlow 2 and Keras" will provide you with a comprehensive overview of deep learning and TensorFlow, which will help you understand the concepts covered in the course and apply them to your own projects.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Take notes
- Complete the exercises
Follow a tutorial on object recognition with TensorFlow
Show steps
Following a tutorial on object recognition with TensorFlow will give you a step-by-step guide to building a model, which can help you learn the process and apply it to your own projects.
Show steps
Show steps
Show steps
- Find a tutorial on object recognition with TensorFlow
- Follow the steps in the tutorial
- Experiment with the code and try to understand how it works
Create a compilation of resources on object recognition
Show steps
Creating a compilation of resources on object recognition will help you organize and synthesize the information you learn in the course and make it easier to access and review later.
Show steps
Show steps
Show steps
- Gather resources on object recognition
- Organize the resources into a logical structure
- Create a document or website to share the compilation
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice building simple CNN models
Show steps
Practicing building simple CNN models will help you develop a deeper understanding of how they work and how to use them effectively.
Show steps
Show steps
Show steps
- Find a dataset of images
- Load the dataset into TensorFlow
- Create a CNN model
- Train the model
- Evaluate the model
Attend a workshop on TensorFlow
Show steps
Attending a workshop on TensorFlow will give you hands-on experience with the framework and help you learn how to use it effectively.
Show steps
Show steps
Show steps
- Find a workshop on TensorFlow
- Attend the workshop
- Complete the exercises
Attend a meetup or conference on deep learning
Show steps
Attending a meetup or conference on deep learning will allow you to connect with other people who are interested in the field and learn about their work.
Show steps
Show steps
Show steps
- Find a meetup or conference on deep learning
- Attend the event
- Network with other attendees
Volunteer to help with a project related to deep learning
Show steps
Volunteering to help with a project related to deep learning will give you practical experience and allow you to contribute to the community.
Show steps
Show steps
Show steps
- Find a project related to deep learning
- Contact the project organizers
- Volunteer your time
Create a model in TensorFlow
Show steps
Creating a model in TensorFlow will allow you to apply the concepts you learn in the course and deepen your understanding of object recognition.
Show steps
Show steps
Show steps
- Gather the necessary data
- Prepare the data for training
- Build the model architecture
- Train the model
- Evaluate the model
Start a project to build an object recognition system
Show steps
Starting a project to build an object recognition system will allow you to apply the concepts you learn in the course and develop a real-world application.
Show steps
Show steps
Show steps
- Define the scope of your project
- Gather the necessary resources
- Build the system
- Test the system
- Deploy the system
Career center
Learners who complete TensorFlow for CNNs: Object Recognition will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
The TensorFlow for CNNs: Object Recognition course directly aligns with the responsibilities of a Computer Vision Engineer, who specializes in developing, deploying, and maintaining computer vision systems. This course offers practical experience in building object recognition models using TensorFlow, equipping learners with the skills to tackle real-world computer vision challenges effectively.
Deep Learning Engineer
Deep Learning Engineer
This project-based course aligns closely with the role of a Deep Learning Engineer, focusing on building deep learning models for object recognition using TensorFlow. The hands-on experience gained in this course can help Deep Learning Engineers enhance their skills in designing, implementing, and optimizing deep learning architectures for computer vision applications.
Machine Learning Engineer
Machine Learning Engineer
TensorFlow for CNNs: Object Recognition provides Machine Learning Engineers with a solid foundation in building and training convolutional neural networks for object recognition tasks. The course's emphasis on practical implementation using real-world data provides valuable experience that can enhance their ability to develop robust and effective machine learning models.
Software Engineer
Software Engineer
Software Engineers working in the field of computer vision or deep learning may find this course highly relevant. TensorFlow for CNNs: Object Recognition offers practical experience in building object recognition models using TensorFlow, providing Software Engineers with the skills to develop and implement computer vision solutions.
Student
Student
Students pursuing a degree in computer science, data science, or a related field may find this course beneficial. TensorFlow for CNNs: Object Recognition provides hands-on experience in building object recognition models using TensorFlow, complementing theoretical knowledge and preparing students for careers in these fields.
Researcher
Researcher
This course could be valuable for Researchers in computer vision or deep learning. By providing practical experience in building object recognition models using TensorFlow, it can help Researchers explore new approaches and contribute to the advancement of computer vision technologies.
Data Scientist
Data Scientist
A TensorFlow-based object recognition model can be a valuable tool for a Data Scientist, facilitating the analysis and interpretation of large, complex datasets, particularly those involving visual data. This project-based course provides hands-on experience in building such models, enabling Data Scientists to enhance their skillset and advance their capabilities in handling real-world data.
Consultant
Consultant
Consultants specializing in computer vision or deep learning may find this course useful. By providing practical experience in building object recognition models using TensorFlow, it can help Consultants enhance their technical skills and provide valuable insights to clients in these fields.
Data Analyst
Data Analyst
TensorFlow for CNNs: Object Recognition can be beneficial for Data Analysts looking to expand their skills in data analysis and visualization. This course provides practical experience in building object recognition models using TensorFlow, enabling Data Analysts to extract meaningful insights from visual data and enhance their overall data analysis capabilities.
Entrepreneur
Entrepreneur
Entrepreneurs looking to develop computer vision or deep learning-based products may find this course beneficial. TensorFlow for CNNs: Object Recognition provides practical experience in building object recognition models using TensorFlow, equipping Entrepreneurs with the technical skills to bring their product ideas to life.
Product Manager
Product Manager
Product Managers responsible for developing computer vision or deep learning products may benefit from this course. TensorFlow for CNNs: Object Recognition provides insights into the practical aspects of building object recognition models using TensorFlow, enabling Product Managers to make informed decisions and guide product development.
Technical Writer
Technical Writer
Technical Writers specializing in computer vision or deep learning may find this course beneficial. By providing practical experience in building object recognition models using TensorFlow, it offers insights into the technical concepts and applications of these technologies, enabling Technical Writers to create accurate and informative documentation.
Educator
Educator
Educators in computer science or related fields may find this course valuable. TensorFlow for CNNs: Object Recognition provides practical experience in building object recognition models using TensorFlow, empowering Educators to stay up-to-date with the latest technologies and effectively teach these concepts to students.
Project Manager
Project Manager
Project Managers involved in computer vision or deep learning projects may find this course useful. TensorFlow for CNNs: Object Recognition provides insights into the practical aspects of building object recognition models using TensorFlow, enabling Project Managers to effectively manage and coordinate project teams.
Business Analyst
Business Analyst
Business Analysts working in the field of computer vision or deep learning may find this course helpful. By providing practical experience in building object recognition models using TensorFlow, it offers insights into the technical aspects of these technologies, enabling Business Analysts to make informed recommendations and support decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/3oeplv/tensorflow
Reading list
We've selected 12 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
TensorFlow for CNNs: Object Recognition.
Save
This paper introduces the AlexNet architecture, a deep convolutional neural network that achieved state-of-the-art results on the ImageNet Large Scale Visual Recognition Challenge in 2012. It provides valuable insights into the design and training of deep convolutional neural networks for object recognition tasks.
Comprehensive guide to using TensorFlow for deep learning. It covers all the basics of TensorFlow, including how to create and train models, and also provides helpful tips and tricks for building and deploying deep learning applications.
Provides a hands-on introduction to deep learning from scratch. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It great resource for learners who want to get a deeper understanding of how deep learning works and how to implement deep learning models from scratch.
Provides a comprehensive introduction to deep learning, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks. It great resource for learners who want to get started with deep learning and build their own object recognition models.
Provides a comprehensive introduction to object recognition with local features. It covers topics such as feature detection, feature descriptors, and object matching. It great resource for learners who want to get a deeper understanding of the fundamentals of object recognition.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for learners who want to get a deeper understanding of the fundamentals of machine learning.
Provides a practical introduction to deep learning for computer vision tasks. It covers the fundamental concepts of deep learning and explains how to use popular deep learning frameworks to build and train computer vision models.
Provides a comprehensive introduction to deep learning for vision systems. It covers topics such as image processing, feature extraction, and object detection. It great resource for learners who want to get started with deep learning for vision systems and build their own object recognition models.
Save
Provides a practical introduction to computer vision using OpenCV, a popular open-source library for computer vision. It covers topics such as image processing, feature detection, object tracking, and machine learning for computer vision.
Save
Covers the fundamental principles of computer vision and provides an overview of the key algorithms and techniques used in this field. It good starting point for learners who want to gain a comprehensive understanding of computer vision and its applications.
Provides a visual introduction to deep learning. It uses a variety of diagrams and illustrations to explain the concepts of deep learning. It great resource for learners who want to get a better understanding of how deep learning works.
Provides a practical introduction to machine learning using Scikit-Learn and TensorFlow, two popular Python libraries for machine learning. It covers topics such as data preprocessing, feature engineering, model selection, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3oeplv/tensorflow
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Mo Rebaie
Good to know
Suitable for beginners who seek a practical project with TensorFlow
Develops skills in Python and object recognition tasks, valuable for data scientists
Provides bonus deep learning exercise with TensorFlow for advanced learners
May require learners to have basic understanding of deep learning
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