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Mo Rebaie
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
Read more
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
Read more
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
In this 1.5-hour long project-based course, you will learn practically how to work on a 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 created a deep neural network with TensorFlow on a real-world dataset.
This class is for learners who want to use Python for building 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/ln6y9b/tensorflow
What's inside
Syllabus
Tensorflow for AI: Neural Network Representation
Welcome to this project-based course on Neural Network Representation.This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate offered at Coursera, which will help learners reinforce their skills and build more projects with Tensorflow. In this 1-hour long project-based course, you will learn practically how to work on a 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 created a deep neural network with TensorFlow on a real-world dataset.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides updated knowledge in neural network concepts and reinforces learners' skills
Helps build a stronger foundation for learners with basic deep learning knowledge
Provides the opportunity to create a real-world project using TensorFlow
Taught by experienced instructors who are recognized for their expertise in TensorFlow
Demonstrates practical application of deep learning with TensorFlow
Save this course
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Reviews summary
Tensorflow with room for improvement
According to students, TensorFlow for AI: Neural Network Representation is an okay course. The lectures and instructor are engaging. However, students wish that the videos were larger and more visible. Scroll down to see notes with excerpts for more details.
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 AI: Neural Network Representation with these
activities:
Course Resource Compilation
Show steps
Helps you stay organized and engaged with the course materials.
Browse courses on
TensorFlow
Show steps
-
Download and save all course materials, including lecture notes, slides, and assignments.
-
Create a system for organizing your materials, both physically and digitally.
Python Programming Refresher
Show steps
Strengthens your Python programming skills, which are essential for working with TensorFlow.
Browse courses on
Python
Show steps
-
Review the basics of Python syntax and data structures.
-
Practice writing simple Python programs.
Deep Learning with Python
Show steps
Helps solidify your understanding of deep learning and Python, which will be covered in the course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Complete the exercises and projects in the book.
-
Review the chapters on TensorFlow and Keras.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow-Based Image Classifier
Show steps
Provides you with hands-on experience building and training a deep learning model in TensorFlow.
Browse courses on
TensorFlow
Show steps
-
Gather and prepare a dataset of images.
-
Create a TensorFlow model for image classification.
-
Train and evaluate your model.
TensorFlow Developer Professional Certificate Course Series Overview
Show steps
Helps further familiarize yourself with the TensorFlow Developer Professional Certificate Course Series by reviewing course overviews
Browse courses on
TensorFlow
Show steps
-
Review the TensorFlow Developer Professional Certificate Course Series overview and browse the course catalog.
-
Research the full list of courses and complete any that fit your interests and career goals.
TensorFlow Coding Exercises
Show steps
Improves your ability to apply TensorFlow concepts through hands-on coding exercises.
Browse courses on
TensorFlow
Show steps
-
Find online TensorFlow coding exercises or create your own.
-
Solve the exercises and review your solutions.
Sample Project Portfolio
Show steps
Prepares you for the demands of the course project by creating a portfolio of Deep Learning projects in TensorFlow
Browse courses on
TensorFlow
Show steps
-
Identify the projects you want to showcase in your portfolio.
-
Create a well-documented portfolio website or document.
-
Build each project, highlighting your skills in TensorFlow and deep learning.
Meetups and Conferences
Show steps
Connects you with other professionals in the field and exposes you to cutting-edge research and applications.
Browse courses on
TensorFlow
Show steps
-
Research and identify relevant meetups and conferences.
-
Attend events and engage with speakers and attendees.
Course Resource Compilation
Show steps
Helps you stay organized and engaged with the course materials.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Download and save all course materials, including lecture notes, slides, and assignments.
- Create a system for organizing your materials, both physically and digitally.
Python Programming Refresher
Show steps
Strengthens your Python programming skills, which are essential for working with TensorFlow.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax and data structures.
- Practice writing simple Python programs.
Deep Learning with Python
Show steps
Helps solidify your understanding of deep learning and Python, which will be covered in the course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Complete the exercises and projects in the book.
- Review the chapters on TensorFlow and Keras.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow-Based Image Classifier
Show steps
Provides you with hands-on experience building and training a deep learning model in TensorFlow.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Gather and prepare a dataset of images.
- Create a TensorFlow model for image classification.
- Train and evaluate your model.
TensorFlow Developer Professional Certificate Course Series Overview
Show steps
Helps further familiarize yourself with the TensorFlow Developer Professional Certificate Course Series by reviewing course overviews
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review the TensorFlow Developer Professional Certificate Course Series overview and browse the course catalog.
- Research the full list of courses and complete any that fit your interests and career goals.
TensorFlow Coding Exercises
Show steps
Improves your ability to apply TensorFlow concepts through hands-on coding exercises.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find online TensorFlow coding exercises or create your own.
- Solve the exercises and review your solutions.
Sample Project Portfolio
Show steps
Prepares you for the demands of the course project by creating a portfolio of Deep Learning projects in TensorFlow
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify the projects you want to showcase in your portfolio.
- Create a well-documented portfolio website or document.
- Build each project, highlighting your skills in TensorFlow and deep learning.
Meetups and Conferences
Show steps
Connects you with other professionals in the field and exposes you to cutting-edge research and applications.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Research and identify relevant meetups and conferences.
- Attend events and engage with speakers and attendees.
Career center
Learners who complete TensorFlow for AI: Neural Network Representation will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A *Machine Learning Engineer* designs, develops, and maintains machine learning models. They work with data scientists to identify business problems that can be solved with machine learning, and then they develop and implement the models that solve those problems. This course provides a solid foundation for this career path by teaching you the fundamentals of TensorFlow and how to use it for deep learning. You'll learn how to create, train, and test neural networks, and you'll gain experience working with real-world data. This course could improve your qualifications for machine learning engineer positions by improving your knowledge of real-world applications of TensorFlow.
Deep Learning Engineer
Deep Learning Engineer
A *Deep Learning Engineer* designs, develops, and maintains deep learning models. They work with a variety of programming languages and technologies to create deep learning models that can solve a variety of problems, such as image recognition, natural language processing, and speech recognition. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning. This course could improve your qualifications as a deep learning engineer by helping you develop the skills you need to create, train, and test deep learning models.
Data Scientist
Data Scientist
A *Data Scientist* uses data to solve business problems. They collect, clean, and analyze data from a variety of sources, and then they use that data to build models that can predict future outcomes or make recommendations. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for data scientists. This course could improve your qualifications as a data scientist by helping you develop the skills you need to create, train, and test deep learning models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An *Artificial Intelligence Engineer* designs, develops, and maintains artificial intelligence systems. They work with a variety of programming languages and technologies to create artificial intelligence systems that can perform tasks such as natural language processing, computer vision, and machine learning. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for artificial intelligence engineers. This course could improve your qualifications as an artificial intelligence engineer by helping you develop the skills you need to create, train, and test deep learning models for artificial intelligence applications.
Natural Language Processing Engineer
Natural Language Processing Engineer
A *Natural Language Processing Engineer* designs, develops, and maintains natural language processing systems. They work with a variety of programming languages and technologies to create natural language processing systems that can perform tasks such as text classification, sentiment analysis, and machine translation. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for natural language processing engineers. This course could improve your qualifications as a natural language processing engineer by helping you develop the skills you need to create, train, and test deep learning models for natural language processing tasks.
Speech Recognition Engineer
Speech Recognition Engineer
A *Speech Recognition Engineer* designs, develops, and maintains speech recognition systems. They work with a variety of programming languages and technologies to create speech recognition systems that can perform tasks such as voice recognition, speech-to-text transcription, and speaker recognition. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for speech recognition engineers. This course could improve your qualifications as a speech recognition engineer by helping you develop the skills you need to create, train, and test deep learning models for speech recognition tasks.
Computer Vision Engineer
Computer Vision Engineer
A *Computer Vision Engineer* designs, develops, and maintains computer vision systems. They work with a variety of programming languages and technologies to create computer vision systems that can perform tasks such as object recognition, image classification, and video analysis. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for computer vision engineers. This course could improve your qualifications as a computer vision engineer by helping you develop the skills you need to create, train, and test deep learning models for computer vision tasks.
Software Engineer
Software Engineer
A *Software Engineer* designs, develops, and maintains software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course provides a strong foundation in TensorFlow, which is a popular deep learning library. This course could make you a more competitive candidate for software engineering positions by improving your knowledge of deep learning and TensorFlow.
Robotics Engineer
Robotics Engineer
A *Robotics Engineer* designs, develops, and maintains robots. They work with a variety of programming languages and technologies to create robots that can perform tasks such as navigation, object manipulation, and human-robot interaction. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for robotics engineers. This course could improve your qualifications as a robotics engineer by helping you develop the skills you need to create, train, and test deep learning models for robotics applications.
Consultant
Consultant
A *Consultant* provides advice and expertise to clients. They work with a variety of clients to identify problems and develop solutions. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for consultants. This course could improve your qualifications as a consultant by helping you develop the skills you need to create, train, and test deep learning models for consulting tasks.
Business Analyst
Business Analyst
A *Business Analyst* analyzes business processes and identifies areas for improvement. They work with a variety of stakeholders to gather requirements and develop solutions that can improve business performance. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for business analysts. This course could improve your qualifications as a business analyst by helping you develop the skills you need to create, train, and test deep learning models for business analysis tasks.
Data Analyst
Data Analyst
A *Data Analyst* collects, cleans, and analyzes data to identify trends and patterns. They work with a variety of programming languages and technologies to create data visualizations and reports that can be used to make informed decisions. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for data analysts. This course could improve your qualifications as a data analyst by helping you develop the skills you need to create, train, and test deep learning models for data analysis tasks.
Product Manager
Product Manager
A *Product Manager* manages the development and launch of new products. They work with a variety of stakeholders to gather requirements and develop a product roadmap. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for product managers. This course could improve your qualifications as a product manager by helping you develop the skills you need to create, train, and test deep learning models for product development tasks.
Project Manager
Project Manager
A *Project Manager* plans and executes projects. They work with a variety of stakeholders to define project scope, develop a project plan, and track project progress. This course provides a foundational understanding of TensorFlow and how it can be used for deep learning, which is a valuable skill for project managers. This course could improve your qualifications as a project manager by helping you develop the skills you need to create, train, and test deep learning models for project management tasks.
Research Scientist
Research Scientist
A *Research Scientist* conducts systematic investigations to add to knowledge about the natural world and human society. Their research could be in a wide range of fields including biology, chemistry, and information and computer science. This course helps build a foundation for this career path, introducing the fundamentals of TensorFlow and how it can be used in deep learning to analyze data and solve problems. This course may also be of interest to someone in this career role because it covers topics such as neural network representation, training, and testing.
For more career information including salaries, visit:
OpenCourser.com/course/ln6y9b/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 AI: Neural Network Representation.
Save
This comprehensive textbook provides a rigorous treatment of deep learning theory and algorithms. It covers advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, making it suitable for researchers and advanced practitioners.
This textbook provides a comprehensive introduction to deep learning, covering fundamental concepts, neural network architectures, and advanced techniques. It is an excellent resource for learners who want to gain a strong foundation in deep learning and build practical applications using Python.
This textbook provides a comprehensive treatment of the mathematical foundations of machine learning. It covers topics such as linear algebra, probability theory, and optimization, making it suitable for advanced learners and researchers seeking a deeper understanding of the mathematical underpinnings of deep learning.
This textbook provides a theoretical foundation for machine learning algorithms. It covers topics such as statistical learning theory, optimization techniques, and model evaluation, making it suitable for advanced learners and researchers.
Offers a hands-on guide to TensorFlow, the popular deep learning framework from Google. It provides clear explanations of core concepts, practical examples, and case studies, making it suitable for both beginners and experienced deep learning practitioners.
This textbook provides a comprehensive treatment of neural networks and deep learning concepts. It covers both theoretical foundations and practical applications, making it suitable for advanced learners and researchers seeking a thorough understanding of deep learning.
Save
Provides a comprehensive introduction to deep learning using the R programming language. It covers fundamental concepts, neural network architectures, and advanced techniques, making it suitable for both beginners and experienced deep learning practitioners.
Save
Provides a hands-on introduction to TensorFlow, covering essential concepts, model building, and debugging techniques. It valuable resource for beginners and intermediate learners seeking to build and deploy their own deep learning models.
This practical guide covers essential machine learning concepts and techniques, including data preprocessing, model selection, and evaluation. It focuses on using Python's Scikit-Learn, Keras, and TensorFlow libraries for real-world applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ln6y9b/tensorflow
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Mo Rebaie
Language
English
Good to know
Provides updated knowledge in neural network concepts and reinforces learners' skills
Helps build a stronger foundation for learners with basic deep learning knowledge
Provides the opportunity to create a real-world project using TensorFlow
Taught by experienced instructors who are recognized for their expertise in TensorFlow
Demonstrates practical application of deep learning with TensorFlow
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