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TensorFlow for CNNs
Multi-Class Classification
Mo Rebaie
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 how to build a multi-class Classifier in CNNs using a pre-trained model trained on the much larger dataset, and you will learn practically how to solve a multi-image classification deep learning task in the real world and create, train, and test a neural network with Tensorflow using real-world images, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned how to build a multi-class classifier in convolutional neural networks 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 building multi-class classifier using convolutional neural networks 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 project. 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/win4h6/tensorflow
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops expertise in using Tensorflow to work with Convolutional Neural Networks
Provides an opportunity to build real-world projects with Tensorflow
Taught by Mo Rebaie, an expert in Tensorflow and Convolutional Neural Networks
Reinforces skills from the second course of DeepLearning.AI TensorFlow Developer Professional Certificate
Suitable for learners with a basic understanding of deep learning or those who have completed a basic deep learning course
Provides a hands-on learning experience with real-world images
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Reviews summary
Tensorflow for cnns: learn to classify images
TensorFlow for CNNs: Multi-Class Classification is a beginner-friendly course that explores how to use TensorFlow for image classification. While reviewers praise the course for being easy to understand and informative, some note that the exam questions could be improved.
Course is easy to understand.
"Course was easily understood"
Exam questions could use improvement.
"some of the questions on the exam were not clear and could use some polishing"
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: Multi-Class Classification with these
activities:
Review prerequisites
Show steps
Reinforce your deep learning foundation and ensure you're starting the course with a solid understanding of essential concepts.
Browse courses on
Deep Learning
Show steps
-
Revisit basic linear algebra concepts, such as vectors and matrices.
-
Review the basics of calculus, particularly differentiation and integration.
-
Refresh your understanding of probability and statistics.
Review coding exercises
Show steps
Practice coding by completing coding exercises.
Browse courses on
Python Programming
Show steps
-
Solve Python coding exercises.
-
Debug your solutions.
-
Review your code for efficiency.
Review CNN fundamentals
Show steps
Review the basics of convolutional neural networks (CNNs) to strengthen your understanding.
Browse courses on
Convolutional Neural Networks
Show steps
-
Read about CNNs in a textbook or online.
-
Watch video tutorials on CNNs.
-
Complete practice problems on CNNs.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Practice coding exercises
Show steps
Improve your coding skills through practice.
Browse courses on
Python Programming
Show steps
-
Find coding exercises online or in textbooks.
-
Solve the exercises on your own.
-
Review your solutions and identify areas for improvement.
Join a study group
Show steps
Connect with other learners and discuss course concepts.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course material.
-
Work together on practice problems.
Join study groups or discussion forums
Show steps
Engage with peers to reinforce your understanding and gain diverse perspectives.
Browse courses on
CNNs
Show steps
-
Join online study groups or discussion forums dedicated to CNNs and TensorFlow.
-
Participate in discussions, ask questions, and share your knowledge.
-
Collaborate with peers on mini-projects or problem-solving exercises.
Follow tutorials on building CNNs
Show steps
Supplement your course learning by exploring practical tutorials that guide you through the process of building and training CNNs.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find reputable online tutorials or courses on CNNs.
-
Work through the tutorials, hands-on, to build your own CNN models.
-
Experiment with different CNN architectures and hyperparameters.
Build a CNN from scratch
Show steps
Gain hands-on experience by building a CNN from scratch.
Show steps
-
Find a tutorial on building a CNN.
-
Follow the tutorial step-by-step.
-
Test your CNN on a dataset.
Contribute to a CNN project
Show steps
Gain practical experience and connect with the wider machine learning community.
Show steps
-
Identify an open source CNN project.
-
Find a way to contribute to the project.
-
Submit a pull request with your contributions.
Practice coding CNNs in Python
Show steps
Solidify your coding skills by practicing the implementation of CNNs in Python.
Browse courses on
Python
Show steps
-
Find coding exercises or challenges related to CNNs.
-
Work through the exercises, coding solutions from scratch.
-
Debug and refine your code to improve its efficiency and accuracy.
Develop a CNN for a real-world problem
Show steps
Apply your skills to a real-world problem by developing a CNN for a specific task.
Show steps
-
Identify a real-world problem that can be solved using CNNs.
-
Collect and prepare a dataset for your problem.
-
Design and implement a CNN to solve your problem.
-
Evaluate the performance of your CNN.
Mentor junior learners or contribute to online communities
Show steps
Solidify your knowledge by sharing it with others and contributing to the community.
Browse courses on
CNNs
Show steps
-
Identify opportunities to mentor junior learners or participate in online forums.
-
Provide guidance, answer questions, and share your expertise.
-
Contribute to open-source projects or create learning resources for others.
Mentor junior learners
Show steps
Share your knowledge and support other learners.
Show steps
-
Volunteer to mentor other learners.
-
Meet with mentees regularly to provide guidance.
-
Answer questions and help mentees overcome challenges.
Review prerequisites
Show steps
Reinforce your deep learning foundation and ensure you're starting the course with a solid understanding of essential concepts.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Revisit basic linear algebra concepts, such as vectors and matrices.
- Review the basics of calculus, particularly differentiation and integration.
- Refresh your understanding of probability and statistics.
Review coding exercises
Show steps
Practice coding by completing coding exercises.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Solve Python coding exercises.
- Debug your solutions.
- Review your code for efficiency.
Review CNN fundamentals
Show steps
Review the basics of convolutional neural networks (CNNs) to strengthen your understanding.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Read about CNNs in a textbook or online.
- Watch video tutorials on CNNs.
- Complete practice problems on CNNs.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Practice coding exercises
Show steps
Improve your coding skills through practice.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Find coding exercises online or in textbooks.
- Solve the exercises on your own.
- Review your solutions and identify areas for improvement.
Join a study group
Show steps
Connect with other learners and discuss course concepts.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course material.
- Work together on practice problems.
Join study groups or discussion forums
Show steps
Engage with peers to reinforce your understanding and gain diverse perspectives.
Browse courses on
CNNs
Show steps
Show steps
Show steps
- Join online study groups or discussion forums dedicated to CNNs and TensorFlow.
- Participate in discussions, ask questions, and share your knowledge.
- Collaborate with peers on mini-projects or problem-solving exercises.
Follow tutorials on building CNNs
Show steps
Supplement your course learning by exploring practical tutorials that guide you through the process of building and training CNNs.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find reputable online tutorials or courses on CNNs.
- Work through the tutorials, hands-on, to build your own CNN models.
- Experiment with different CNN architectures and hyperparameters.
Build a CNN from scratch
Show steps
Gain hands-on experience by building a CNN from scratch.
Show steps
Show steps
Show steps
- Find a tutorial on building a CNN.
- Follow the tutorial step-by-step.
- Test your CNN on a dataset.
Contribute to a CNN project
Show steps
Gain practical experience and connect with the wider machine learning community.
Show steps
Show steps
Show steps
- Identify an open source CNN project.
- Find a way to contribute to the project.
- Submit a pull request with your contributions.
Practice coding CNNs in Python
Show steps
Solidify your coding skills by practicing the implementation of CNNs in Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find coding exercises or challenges related to CNNs.
- Work through the exercises, coding solutions from scratch.
- Debug and refine your code to improve its efficiency and accuracy.
Develop a CNN for a real-world problem
Show steps
Apply your skills to a real-world problem by developing a CNN for a specific task.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using CNNs.
- Collect and prepare a dataset for your problem.
- Design and implement a CNN to solve your problem.
- Evaluate the performance of your CNN.
Mentor junior learners or contribute to online communities
Show steps
Solidify your knowledge by sharing it with others and contributing to the community.
Browse courses on
CNNs
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners or participate in online forums.
- Provide guidance, answer questions, and share your expertise.
- Contribute to open-source projects or create learning resources for others.
Mentor junior learners
Show steps
Share your knowledge and support other learners.
Show steps
Show steps
Show steps
- Volunteer to mentor other learners.
- Meet with mentees regularly to provide guidance.
- Answer questions and help mentees overcome challenges.
Career center
Learners who complete TensorFlow for CNNs: Multi-Class Classification will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Machine Learning Engineers because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Machine Learning Engineers develop the skills they need to succeed in their careers.
Data Scientist
Data Scientist
Data Scientists analyze data to find insights that can help organizations make better decisions. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Data Scientists because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Data Scientists develop the skills they need to succeed in their careers.
Research Scientist
Research Scientist
Research Scientists develop new technologies and applications. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Research Scientists because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Research Scientists develop the skills they need to succeed in their careers.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Computer Vision Engineers because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Computer Vision Engineers develop the skills they need to succeed in their careers.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Statisticians because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Statisticians develop the skills they need to succeed in their careers.
Management Consultant
Management Consultant
Management Consultants help organizations improve their performance. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Management Consultants because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Management Consultants develop the skills they need to succeed in their careers.
Data Analyst
Data Analyst
Data Analysts analyze data to find insights that can help organizations make better decisions. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Data Analysts because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Data Analysts develop the skills they need to succeed in their careers.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Financial Analysts because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Financial Analysts develop the skills they need to succeed in their careers.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Product Managers because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Product Managers develop the skills they need to succeed in their careers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Quantitative Analysts because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Quantitative Analysts develop the skills they need to succeed in their careers.
Actuary
Actuary
Actuaries analyze risk and uncertainty. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Actuaries because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Actuaries develop the skills they need to succeed in their careers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Software Engineers because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Software Engineers develop the skills they need to succeed in their careers.
Project Manager
Project Manager
Project Managers oversee the planning and execution of projects. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Project Managers because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Project Managers develop the skills they need to succeed in their careers.
Business Analyst
Business Analyst
Business Analysts analyze business processes to find ways to improve them. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Business Analysts because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Business Analysts develop the skills they need to succeed in their careers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. The TensorFlow for CNNs: Multi-Class Classification course can be useful for Operations Research Analysts because it teaches them how to build and train convolutional neural networks, which are a powerful tool for image classification. This course can help Operations Research Analysts develop the skills they need to succeed in their careers.
For more career information including salaries, visit:
OpenCourser.com/course/win4h6/tensorflow
Reading list
We've selected nine 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: Multi-Class Classification.
Provides a comprehensive overview of TensorFlow 2 and Keras, including deep learning fundamentals and advanced techniques. It valuable resource for learners who want to enhance their understanding of deep learning and gain practical experience with TensorFlow.
Practical guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preparation, model selection, and evaluation.
Concise and accessible introduction to deep learning with Python and Keras. It covers the basics of deep learning, as well as practical tips and techniques.
Save
Practical guide to building and deploying deep learning models. It covers a wide range of topics, including data preprocessing, model training, and model evaluation.
These notes provide a concise and practical introduction to deep learning with Python and Keras. They cover the basics of deep learning, as well as practical tips and techniques.
Save
Comprehensive overview of convolutional neural networks. It covers the basics of convolutional neural networks, as well as advanced topics such as transfer learning and generative adversarial networks.
Comprehensive overview of pattern recognition and machine learning. It covers the basics of pattern recognition and machine learning, as well as advanced topics such as Bayesian inference and support vector machines.
Save
Comprehensive overview of machine learning from an algorithmic perspective. It covers the basics of machine learning, as well as advanced topics such as reinforcement learning and natural language processing.
Comprehensive overview of reinforcement learning. It covers the basics of reinforcement learning, as well as advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/win4h6/tensorflow
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Mo Rebaie
Good to know
Develops expertise in using Tensorflow to work with Convolutional Neural Networks
Provides an opportunity to build real-world projects with Tensorflow
Taught by Mo Rebaie, an expert in Tensorflow and Convolutional Neural Networks
Reinforces skills from the second course of DeepLearning.AI TensorFlow Developer Professional Certificate
Suitable for learners with a basic understanding of deep learning or those who have completed a basic deep learning course
Provides a hands-on learning experience with real-world images
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