TensorFlow for AI: Applying Image Convolution from Coursera

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 discover convolutions, apply filters to images, apply pooling layers, and try out the convolution and pooling techniques on real images to learn about how convolutions work. At the end of the project, you will get a bonus deep learning project implemented with Tensorflow. By the end of this project, you will have learned how convolutions work and how to create convolutional layers to prepare for your own deep learning projects using convolutional neural networks.

This class is for learners who want to use Python for building 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 knowledge-based course about convolutions in images with TensorFlow. Also, this project provides learners with needed knowledge about building convolutional neural networks and improves their skills in applying filters to images which helps them in fulfilling their career goals by adding this project to their portfolios.

What's inside

Syllabus

Tensorflow for AI: Applying Image Convolution

Welcome to this project-based course on Applying Image Convolution. 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 how to create convolutions and pooling layers with filters and build convolutional layers for convolutional neural networks with Tensorflow, and you will get a bonus deep learning project implemented with Tensorflow. By the end of this project, you will have applied convolutions with filters and pooling layers for neural networks.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides practical applications of Convolutional layers for learners

Enhances learners' skills in applying filters to images using Tensorflow

Builds a strong foundation for building Convolutional Neural Networks

Provides a practical deep learning project with Tensorflow for learners to practice

Supports learners with basic deep learning knowledge or those seeking specific knowledge about convolutions

May not be suitable for complete beginners to deep learning

Reviews summary

Tensorflow training

According to students, TensorFlow for AI: Applying Image Convolution provides clear and comprehensable visual demonstrations of convolutional operations. Learners say this training is helpful for those seeking to land a job in AI.

The course is seen as helpful by students.

"very useful to land into a job"

Students appreciated the visual demonstrations.

"The way to explain convolutional operations is right and well demonstrated with visual results that are pretty comprehensible for any one person."

"A very nice explanation of convolutional and pooling operations accompanied by visual results that are comprehensible and easily understandable."

One learner mentioned the need for more hands-on aspects.

"Needs more hands-on."

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: Applying Image Convolution with these activities:

Guided practice with filters and convolutional layers

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Practice with convolutional neural networks will strengthen your understanding of image convolutions.

Browse courses on Convolutional Neural Networks

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Follow the hands-on tutorials provided in the course resources to apply filters on images and create convolutional layers.

Practice applying convolution algorithms

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Reinforce your understanding by working through practice problems and applying convolution algorithms to images.

Browse courses on Convolution

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Identify different types of convolution filters
Apply convolution filters to images

Illustrate the application of convolutions

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By creating your own visual demonstrations, you can solidify your understanding and expand on your knowledge of image convolutions.

Browse courses on Convolutional Neural Networks

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Select a set of images that can showcase the effects of applying different filters and convolutions.
Use image editing software or libraries to apply various convolutions and filters on these images.
Present your results alongside a detailed explanation of the techniques and their outcomes.

Five other activities

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Follow online tutorials on image convolution

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Deepen your understanding by following structured tutorials that provide step-by-step guidance on image convolution.

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Search for online tutorials on image convolution
Select a tutorial that aligns with your skill level
Follow the tutorial's instructions and complete the exercises

Review 'Deep Learning with Python' by Francois Chollet

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Expand your understanding of image convolution by reviewing a comprehensive text that covers the topic in depth.

View Deep Learning with Python, Second Edition on Amazon

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Find a copy of 'Deep Learning with Python'
Read the chapters on image convolution
Complete the exercises provided in the book

Participate in an image recognition hackathon

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Apply your skills in a practical setting by participating in a hackathon that challenges you to solve image recognition problems.

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Find an upcoming image recognition hackathon
Form a team or participate individually
Develop a solution using convolution techniques
Submit your solution and compete with others

Build a custom image classifier using Tensorflow

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Reinforce your skills by developing a practical project that applies convolution to real-world image classification tasks.

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Choose a dataset for your project
Design and train a convolutional neural network
Evaluate the performance of your model
Deploy your model

Document your learning journey

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Reflect on what you have learned and identify areas for improvement by creating a document that summarizes your experience.

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Take notes during the course and while completing activities
Organize your notes into a coherent document
Identify key takeaways and insights
Reflect on areas where you need further development

Career center

Learners who complete TensorFlow for AI: Applying Image Convolution will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers use advanced techniques like deep learning to build and deploy machine learning solutions. TensorFlow is an open-source machine learning library that is used by many companies to develop machine learning models and applications. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Deep Learning Engineer

Deep Learning Engineers design and develop deep learning models for a variety of applications. TensorFlow is a popular library for deep learning, and it is used to develop a variety of applications such as natural language processing, speech recognition, and computer vision. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Data Scientist

Data Scientists use data to solve business problems and make predictions. TensorFlow is a powerful tool for data science, and it can be used to build a variety of machine learning models. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Data Scientist.

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Computer Vision Engineer

Computer Vision Engineers design and develop computer vision systems that can see and interpret the world around them. TensorFlow is a popular library for computer vision, and it is used to develop a variety of applications such as facial recognition, object detection, and medical imaging. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for computer vision tasks. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Computer Vision Engineer.

See salaries and explore the career path for Computer Vision Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers design and develop artificial intelligence systems. TensorFlow is a popular library for machine learning and deep learning, and it is used to develop a variety of artificial intelligence systems. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as an Artificial Intelligence Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

Research Scientist

Research Scientists conduct research in a variety of fields, including machine learning and deep learning. TensorFlow is a popular library for machine learning and deep learning, and it is used by researchers to develop new models and algorithms. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Research Scientist.

See salaries and explore the career path for Research Scientist

Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and models. TensorFlow is a popular library for machine learning and deep learning, and it is used by researchers to develop new models and algorithms. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Machine Learning Researcher.

See salaries and explore the career path for Machine Learning Researcher

Software Engineer

Software Engineers design, develop, and maintain software systems. TensorFlow is a popular library for machine learning and deep learning, and it is used to develop a variety of applications. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Software Engineer.

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Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. TensorFlow is a popular library for data science, and it can be used to build a variety of machine learning models. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make better decisions. TensorFlow is a popular library for data science, and it can be used to build a variety of machine learning models. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Business Intelligence Analyst.

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Data Engineer

Data Engineers design and develop data pipelines that collect, clean, and store data. TensorFlow is a popular library for data science, and it can be used to build a variety of machine learning models. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course will help you build a foundation in TensorFlow and deep learning and prepare you for a career as a Data Engineer.

See salaries and explore the career path for Data Engineer

Product Manager

Product Managers develop and manage software products. TensorFlow is a popular library for machine learning and deep learning, and it is used to develop a variety of software products. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course may help you understand the potential and limitations of machine learning and deep learning, which may be useful for your role as a Product Manager.

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Marketing Analyst

Marketing Analysts use data to understand customer behavior and develop marketing campaigns. TensorFlow is a popular library for data science, and it can be used to build a variety of machine learning models. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course may help you develop a better understanding of how machine learning and deep learning can be used for marketing.

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Sales Manager

Sales Managers lead sales teams and develop sales strategies. TensorFlow is a popular library for machine learning and deep learning, and it is used to develop a variety of software products. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course may help you develop a better understanding of how machine learning and deep learning can be used to improve sales and marketing.

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Project Manager

Project Managers plan and execute projects. TensorFlow is a popular library for machine learning and deep learning, and it is used to develop a variety of software products. This course will help you learn how to use TensorFlow to build convolutional neural networks, which are a type of deep learning model that is used for tasks such as image recognition and object detection. Taking this course may help you develop a better understanding of how machine learning and deep learning can be used in different industries.

See salaries and explore the career path for Project Manager