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Laurence Moroney
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
Read more
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
Read more
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers.
This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/f3kv6d/device
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What's inside
Syllabus
Device-based models with TensorFlow Lite
Welcome to this course on TensorFlow Lite, an exciting technology that allows you to put your models directly and literally into people's hands. You'll start with a deep dive into the technology, and how it works, learning about how you can optimize your models for mobile use -- where battery power and processing power become an important factor. You'll then look at building applications on Android and iOS that use models, and you'll see how to use the TensorFlow Lite Interpreter in these environments. You'll wrap up the course with a look at embedded systems and microcontrollers, running your models on Raspberry Pi and SparkFun Edge boards.
Don't worry if you don't have access to the hardware -- for the most part you'll be able to do everything in emulated environments. So, let's get started by looking at what TensorFlow is and how it works!
Read more
Syllabus
Device-based models with TensorFlow Lite
Welcome to this course on TensorFlow Lite, an exciting technology that allows you to put your models directly and literally into people's hands. You'll start with a deep dive into the technology, and how it works, learning about how you can optimize your models for mobile use -- where battery power and processing power become an important factor. You'll then look at building applications on Android and iOS that use models, and you'll see how to use the TensorFlow Lite Interpreter in these environments. You'll wrap up the course with a look at embedded systems and microcontrollers, running your models on Raspberry Pi and SparkFun Edge boards.
Don't worry if you don't have access to the hardware -- for the most part you'll be able to do everything in emulated environments. So, let's get started by looking at what TensorFlow is and how it works!
Read more
Running a TF model in an Android App
Last week you learned about TensorFlow Lite and you saw how to convert your models from TensorFlow to TensorFlow Lite format. You also learned about the standalone TensorFlow Lite Interpreter which could be used to test these models. You wrapped with an exercise that converted a Fashion MNIST based model to TensorFlow Lite and then tested it with the interpreter.
This week you'll look at the first of the deployment types for this course: Android. Android is a versatile operating system that is used in a number of different device type, but most commonly phones, tablets and TV systems. Using TensorFlow Lite you can run your models on Android, so you can bring ML to any of these device types.
While it helps to understand some Android programming concepts, we hope that you'll be able to follow along even if you don't, and at the very least try out the full sample apps that we'll explore for Image Classification, Object Detection and more!
Building the TensorFLow model on IOS
The other popular mobile operating system is, of course, iOS. So this week you'll do very similar tasks to last week -- learning how to take models and run them on iOS. You'll need some programming background with Swift for iOS to fully understand everything we go through, but even if you don't have this expertise, I think this weeks content is something you'll find fun to explore -- and you'll learn how to build a variety of ML applications that run on this important operating system!
TensorFlow Lite on devices
Now that you've looked at TensorFlow Lite and explored building apps on Android and iOS that use it, the next and final step is to explore embedded systems like Raspberry Pi, and learn how to get your models running on that. The nice thing is that the Pi is a full Linux system, so it can run Python, allowing you to either use the full TensorFlow for Training and Inference, or just the Interpreter for Inference. I'd recommend the latter, as training on a Pi can be slow!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Offers hands-on labs and interactive materials for a better learning experience
Builds upon TensorFlow in Practice Specialization and Deep Learning Specialization, providing a solid foundation for learners with prior knowledge
Introduces TensorFlow Lite, a technology for deploying machine learning models on mobile devices
Provides instructions on preparing models for low-powered devices and executing them on Android and iOS platforms
Covers deployment on embedded systems using TensorFlow on Raspberry Pi and microcontrollers
Assumes prior knowledge in TensorFlow and neural networks, making it suitable for experienced learners
Save this course
Save Device-based Models with TensorFlow Lite to your list so you can find it easily later:
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Reviews summary
Highly-rated tensorflow lite specialization
Learners say this specialization on TensorFlow Lite combines well-rounded content on deploying models on various devices, with strong introductory foundations for a journey into Deep Learning. It has an overall positive sentiment with learners finding it engaging, informative, and well-structured.
Introductory, yet Comprehensive material
"The course is straightforward and very useful. I learned many new concepts regarding Tensorflow Lite in devices."
"Really informative course on tf lite for beginners like me, it has given serious thoughts about the EDGEML field and opportunities"
Clear explanations, excellent demonstrations
"Laurence is a good teacher. His explanations are clear and to the point."
"Well delivered and very interesting course. The code examples and walkthroughs are amazing and help to get you trying this stuff yourself quickly."
Assumes advanced knowledge
"it requires advanced knowledge on app development, even though not mandatory to validate the course."
Lacks mandatory hands-on assignments
"I really loved the TensorFlow in Practice specialization and the first course of the TensorFlow Data and Deployment delivered by M. Moroney. This course is not at all at their level."
"This course is good as overview - theoretical. You go through all the important topics... But unfortunately all you need is to watch. No exercises you would really need to do in order to complete the course."
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 Device-based Models with TensorFlow Lite with these
activities:
TensorFlow Lite Study Group
Show steps
Join or start a study group to discuss and explore TensorFlow Lite concepts with peers. This collaborative activity will enhance your comprehension of the material covered in the 'Device-based models with TensorFlow Lite' section.
Browse courses on
TensorFlow Lite
Show steps
-
Find or create a study group
-
Establish meeting times and frequency
-
Review course materials together
-
Discuss and clarify concepts
-
Share resources and insights
Explore TensorFlow Lite tutorials
Show steps
Deepen your knowledge of TensorFlow Lite by following guided tutorials on various aspects, such as model conversion, deployment, and optimization.
Browse courses on
TensorFlow Lite
Show steps
-
Visit the TensorFlow Lite tutorials website
-
Choose a tutorial that aligns with your learning goals
-
Follow the step-by-step instructions
-
Experiment with different parameters and settings
-
Troubleshoot any errors or issues
Build a Simple Image Classification Model
Show steps
Building a simple image classification model will help you apply the concepts you learn in the course and reinforce your understanding of TensorFlow.
Browse courses on
Image Classification
Show steps
-
Gather a small dataset of images
-
Use TensorFlow to build an image classification model
-
Train and evaluate your model
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Android App Using TensorFlow Lite Image Classification
Show steps
Practice building an Android application that utilizes TensorFlow Lite for image classification. This hands-on activity will reinforce the concepts covered in the 'Running a TF model in an Android App' section.
Browse courses on
Image Classification
Show steps
-
Set up your Android development environment
-
Create a new Android project
-
Add TensorFlow Lite to your project
-
Load and preprocess the image data
-
Create a TensorFlow Lite model for image classification
-
Run the model and display the results
TensorFlow Lite Code Challenges
Show steps
Attempt coding challenges related to TensorFlow Lite to test your understanding and reinforce the concepts covered throughout the course. This self-directed activity will help you identify areas for improvement.
Browse courses on
TensorFlow Lite
Show steps
-
Identify coding challenges online or through platforms
-
Choose challenges that align with your learning objectives
-
Solve the challenges independently or seek assistance
-
Review and analyze your solutions
Practice using TensorFlow Lite Interpreter
Show steps
Familiarize yourself with the TensorFlow Lite Interpreter to enhance your understanding of TensorFlow Lite models and their deployment scenarios.
Show steps
-
Install the TensorFlow Lite Interpreter library
-
Load a pre-trained TensorFlow Lite model
-
Create an interpreter from the loaded model
-
Prepare input data for the model
-
Run inference using the interpreter
Join or host a TensorFlow Lite study group
Show steps
Engage with peers to discuss TensorFlow Lite concepts, share knowledge, and reinforce your understanding through collaborative learning.
Show steps
-
Identify existing TensorFlow Lite study groups or create your own
-
Set regular meeting times and agendas
-
Prepare materials and discussion topics for each session
-
Facilitate discussions and encourage active participation
-
Reflect on your learning and identify areas for improvement
Explore TensorFlow Lite Tutorials
Show steps
Following TensorFlow Lite tutorials will provide you with step-by-step guidance on building and deploying mobile machine learning models.
Browse courses on
TensorFlow Lite
Show steps
-
Go through the official TensorFlow Lite tutorials
-
Complete hands-on exercises
-
Experiment with different models and scenarios
iOS App with TensorFlow Lite for Object Detection
Show steps
Build an iOS application that leverages TensorFlow Lite for object detection. This project-based activity will solidify your understanding of the 'Building the TensorFLow model on IOS' section.
Browse courses on
Object Detection
Show steps
-
Set up your iOS development environment
-
Create a new iOS project
-
Add TensorFlow Lite to your project
-
Load and preprocess the image data
-
Create a TensorFlow Lite model for object detection
-
Run the model and display the results
Develop a machine learning model for a specific domain
Show steps
Enhance your machine learning skills by developing a model tailored to a specific domain or industry, leveraging TensorFlow Lite for deployment.
Show steps
-
Identify a domain or industry of interest
-
Gather and prepare relevant data
-
Explore and select appropriate machine learning algorithms
-
Train and evaluate your machine learning model
-
Deploy your model using TensorFlow Lite for edge devices
Attend Machine Learning Meetups
Show steps
Attending machine learning meetups will allow you to connect with other learners, experts, and professionals in the field.
Browse courses on
Networking
Show steps
-
Find local machine learning meetups
-
Attend meetups regularly
-
Network with other attendees
TensorFlow Lite for Embedded Devices Tutorial
Show steps
Follow an online tutorial to learn how to deploy TensorFlow Lite models on embedded devices like Raspberry Pi. This self-paced activity will complement the 'TensorFlow Lite on devices' section, providing hands-on experience.
Browse courses on
TensorFlow Lite
Show steps
-
Identify a suitable tutorial
-
Gather necessary hardware and software
-
Follow the tutorial instructions
-
Test and deploy your model on the embedded device
Build a mobile app using TensorFlow Lite
Show steps
Apply your knowledge by creating a practical mobile application that leverages TensorFlow Lite models to solve real-world problems.
Browse courses on
Mobile App Development
Show steps
-
Identify a problem or need that can be addressed with machine learning
-
Choose an appropriate TensorFlow Lite model for your application
-
Integrate the TensorFlow Lite model into your mobile app
-
Design and develop the user interface
-
Test and deploy your mobile app
Develop an Android or iOS App with TensorFlow Lite
Show steps
Creating an Android or iOS app with TensorFlow Lite will give you practical experience in deploying machine learning models on mobile devices.
Browse courses on
Mobile App Development
Show steps
-
Choose a mobile platform (Android or iOS)
-
Design and develop your app
-
Implement TensorFlow Lite model integration
-
Test and publish your app
Contribute to TensorFlow Lite open-source projects
Show steps
Enhance your technical skills and contribute to the TensorFlow Lite community by participating in open-source projects.
Browse courses on
Community Engagement
Show steps
-
Identify a project or issue to contribute to
-
Review the project's documentation and guidelines
-
Create a pull request with your proposed changes
-
Collaborate with other contributors and maintainers
-
Merge your contributions and receive feedback
Write a Blog Post on TensorFlow Lite
Show steps
Writing a blog post on TensorFlow Lite will help you solidify your understanding of the concepts and share your knowledge with others.
Browse courses on
Content Creation
Show steps
-
Choose a specific topic related to TensorFlow Lite
-
Research and gather information
-
Write and edit your blog post
-
Publish your blog post
Contribute to TensorFlow Lite Open Source Projects
Show steps
Contributing to TensorFlow Lite open source projects will provide you with valuable hands-on experience and allow you to give back to the community.
Browse courses on
TensorFlow Lite
Show steps
-
Find a suitable open source project
-
Get familiar with the project
-
Identify an area to contribute
-
Make your contributions and submit pull requests
TensorFlow Lite Study Group
Show steps
Join or start a study group to discuss and explore TensorFlow Lite concepts with peers. This collaborative activity will enhance your comprehension of the material covered in the 'Device-based models with TensorFlow Lite' section.
Browse courses on
TensorFlow Lite
Show steps
Show steps
Show steps
- Find or create a study group
- Establish meeting times and frequency
- Review course materials together
- Discuss and clarify concepts
- Share resources and insights
Explore TensorFlow Lite tutorials
Show steps
Deepen your knowledge of TensorFlow Lite by following guided tutorials on various aspects, such as model conversion, deployment, and optimization.
Browse courses on
TensorFlow Lite
Show steps
Show steps
Show steps
- Visit the TensorFlow Lite tutorials website
- Choose a tutorial that aligns with your learning goals
- Follow the step-by-step instructions
- Experiment with different parameters and settings
- Troubleshoot any errors or issues
Build a Simple Image Classification Model
Show steps
Building a simple image classification model will help you apply the concepts you learn in the course and reinforce your understanding of TensorFlow.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Gather a small dataset of images
- Use TensorFlow to build an image classification model
- Train and evaluate your model
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Android App Using TensorFlow Lite Image Classification
Show steps
Practice building an Android application that utilizes TensorFlow Lite for image classification. This hands-on activity will reinforce the concepts covered in the 'Running a TF model in an Android App' section.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Set up your Android development environment
- Create a new Android project
- Add TensorFlow Lite to your project
- Load and preprocess the image data
- Create a TensorFlow Lite model for image classification
- Run the model and display the results
TensorFlow Lite Code Challenges
Show steps
Attempt coding challenges related to TensorFlow Lite to test your understanding and reinforce the concepts covered throughout the course. This self-directed activity will help you identify areas for improvement.
Browse courses on
TensorFlow Lite
Show steps
Show steps
Show steps
- Identify coding challenges online or through platforms
- Choose challenges that align with your learning objectives
- Solve the challenges independently or seek assistance
- Review and analyze your solutions
Practice using TensorFlow Lite Interpreter
Show steps
Familiarize yourself with the TensorFlow Lite Interpreter to enhance your understanding of TensorFlow Lite models and their deployment scenarios.
Show steps
Show steps
Show steps
- Install the TensorFlow Lite Interpreter library
- Load a pre-trained TensorFlow Lite model
- Create an interpreter from the loaded model
- Prepare input data for the model
- Run inference using the interpreter
Join or host a TensorFlow Lite study group
Show steps
Engage with peers to discuss TensorFlow Lite concepts, share knowledge, and reinforce your understanding through collaborative learning.
Show steps
Show steps
Show steps
- Identify existing TensorFlow Lite study groups or create your own
- Set regular meeting times and agendas
- Prepare materials and discussion topics for each session
- Facilitate discussions and encourage active participation
- Reflect on your learning and identify areas for improvement
Explore TensorFlow Lite Tutorials
Show steps
Following TensorFlow Lite tutorials will provide you with step-by-step guidance on building and deploying mobile machine learning models.
Browse courses on
TensorFlow Lite
Show steps
Show steps
Show steps
- Go through the official TensorFlow Lite tutorials
- Complete hands-on exercises
- Experiment with different models and scenarios
iOS App with TensorFlow Lite for Object Detection
Show steps
Build an iOS application that leverages TensorFlow Lite for object detection. This project-based activity will solidify your understanding of the 'Building the TensorFLow model on IOS' section.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Set up your iOS development environment
- Create a new iOS project
- Add TensorFlow Lite to your project
- Load and preprocess the image data
- Create a TensorFlow Lite model for object detection
- Run the model and display the results
Develop a machine learning model for a specific domain
Show steps
Enhance your machine learning skills by developing a model tailored to a specific domain or industry, leveraging TensorFlow Lite for deployment.
Show steps
Show steps
Show steps
- Identify a domain or industry of interest
- Gather and prepare relevant data
- Explore and select appropriate machine learning algorithms
- Train and evaluate your machine learning model
- Deploy your model using TensorFlow Lite for edge devices
Attend Machine Learning Meetups
Show steps
Attending machine learning meetups will allow you to connect with other learners, experts, and professionals in the field.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find local machine learning meetups
- Attend meetups regularly
- Network with other attendees
TensorFlow Lite for Embedded Devices Tutorial
Show steps
Follow an online tutorial to learn how to deploy TensorFlow Lite models on embedded devices like Raspberry Pi. This self-paced activity will complement the 'TensorFlow Lite on devices' section, providing hands-on experience.
Browse courses on
TensorFlow Lite
Show steps
Show steps
Show steps
- Identify a suitable tutorial
- Gather necessary hardware and software
- Follow the tutorial instructions
- Test and deploy your model on the embedded device
Build a mobile app using TensorFlow Lite
Show steps
Apply your knowledge by creating a practical mobile application that leverages TensorFlow Lite models to solve real-world problems.
Browse courses on
Mobile App Development
Show steps
Show steps
Show steps
- Identify a problem or need that can be addressed with machine learning
- Choose an appropriate TensorFlow Lite model for your application
- Integrate the TensorFlow Lite model into your mobile app
- Design and develop the user interface
- Test and deploy your mobile app
Develop an Android or iOS App with TensorFlow Lite
Show steps
Creating an Android or iOS app with TensorFlow Lite will give you practical experience in deploying machine learning models on mobile devices.
Browse courses on
Mobile App Development
Show steps
Show steps
Show steps
- Choose a mobile platform (Android or iOS)
- Design and develop your app
- Implement TensorFlow Lite model integration
- Test and publish your app
Contribute to TensorFlow Lite open-source projects
Show steps
Enhance your technical skills and contribute to the TensorFlow Lite community by participating in open-source projects.
Browse courses on
Community Engagement
Show steps
Show steps
Show steps
- Identify a project or issue to contribute to
- Review the project's documentation and guidelines
- Create a pull request with your proposed changes
- Collaborate with other contributors and maintainers
- Merge your contributions and receive feedback
Write a Blog Post on TensorFlow Lite
Show steps
Writing a blog post on TensorFlow Lite will help you solidify your understanding of the concepts and share your knowledge with others.
Browse courses on
Content Creation
Show steps
Show steps
Show steps
- Choose a specific topic related to TensorFlow Lite
- Research and gather information
- Write and edit your blog post
- Publish your blog post
Contribute to TensorFlow Lite Open Source Projects
Show steps
Contributing to TensorFlow Lite open source projects will provide you with valuable hands-on experience and allow you to give back to the community.
Browse courses on
TensorFlow Lite
Show steps
Show steps
Show steps
- Find a suitable open source project
- Get familiar with the project
- Identify an area to contribute
- Make your contributions and submit pull requests
Career center
Learners who complete Device-based Models with TensorFlow Lite will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are in high demand due to the increasing adoption of AI and machine learning technologies across various industries. This course can provide you with the necessary skills and knowledge to build and deploy machine learning models on mobile devices, which is a rapidly growing field. You will learn how to optimize models for mobile use, build Android and iOS apps that use machine learning models, and deploy models on embedded systems like Raspberry Pi. This course can help you build a solid foundation in mobile machine learning and prepare you for a successful career as a Machine Learning Engineer.
Mobile Application Developer
Mobile Application Developer
Mobile Application Developers are responsible for designing, developing, and maintaining mobile applications. This course can provide you with the skills and knowledge to build mobile applications that use machine learning models. You will learn how to integrate TensorFlow Lite into Android and iOS apps, and how to deploy machine learning models on mobile devices. This course can help you build a strong foundation in mobile application development and prepare you for a successful career as a Mobile Application Developer.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems and make informed decisions. This course can provide you with the skills and knowledge to use machine learning models to analyze data and extract insights. You will learn how to prepare data for machine learning, build and train machine learning models, and deploy models on mobile devices. This course can help you build a strong foundation in data science and prepare you for a successful career as a Data Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can provide you with the skills and knowledge to build software systems that use machine learning models. You will learn how to integrate TensorFlow Lite into software systems, and how to deploy machine learning models on mobile devices. This course can help you build a strong foundation in software engineering and prepare you for a successful career as a Software Engineer.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. This course can provide you with the skills and knowledge to manage the development of machine learning-based products. You will learn how to identify market opportunities for machine learning, build a business case for machine learning products, and manage the development and launch of these products. This course can help you build a strong foundation in product management and prepare you for a successful career as a Product Manager.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems. This course can provide you with the skills and knowledge to use machine learning to solve business problems. You will learn how to identify opportunities for machine learning, build business cases for machine learning solutions, and implement these solutions. This course can help you build a strong foundation in business analysis and prepare you for a successful career as a Business Analyst.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. This course can provide you with the skills and knowledge to use machine learning to analyze data. You will learn how to prepare data for machine learning, build and train machine learning models, and interpret the results of these models. This course can help you build a strong foundation in data analysis and prepare you for a successful career as a Data Analyst.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course can provide you with the skills and knowledge to conduct research in machine learning. You will learn about the latest advances in machine learning, and how to apply these advances to real-world problems. This course can help you build a strong foundation in machine learning research and prepare you for a successful career as a Machine Learning Researcher.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems. This course can provide you with the skills and knowledge to build AI systems that use machine learning models. You will learn about the different types of AI systems, and how to apply machine learning to these systems. This course can help you build a strong foundation in AI engineering and prepare you for a successful career as an Artificial Intelligence Engineer.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, develop, and maintain computer vision systems. This course can provide you with the skills and knowledge to build computer vision systems that use machine learning models. You will learn about the different types of computer vision systems, and how to apply machine learning to these systems. This course can help you build a strong foundation in computer vision engineering and prepare you for a successful career as a Computer Vision Engineer.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design, develop, and maintain natural language processing systems. This course can provide you with the skills and knowledge to build natural language processing systems that use machine learning models. You will learn about the different types of natural language processing systems, and how to apply machine learning to these systems. This course can help you build a strong foundation in natural language processing engineering and prepare you for a successful career as a Natural Language Processing Engineer.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. This course can provide you with the skills and knowledge to build robots that use machine learning models. You will learn about the different types of robots, and how to apply machine learning to these robots. This course can help you build a strong foundation in robotics engineering and prepare you for a successful career as a Robotics Engineer.
Game Developer
Game Developer
Game Developers design, develop, and maintain video games. This course can provide you with the skills and knowledge to build video games that use machine learning models. You will learn about the different types of video games, and how to apply machine learning to these games. This course can help you build a strong foundation in game development and prepare you for a successful career as a Game Developer.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts analyze data to identify trends and patterns in healthcare data. This course can provide you with the skills and knowledge to use machine learning to analyze healthcare data. You will learn how to prepare healthcare data for machine learning, build and train machine learning models, and interpret the results of these models. This course can help you build a strong foundation in healthcare data analysis and prepare you for a successful career as a Healthcare Data Analyst.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to identify trends and patterns. This course can provide you with the skills and knowledge to use machine learning to analyze financial data. You will learn how to prepare financial data for machine learning, build and train machine learning models, and interpret the results of these models. This course can help you build a strong foundation in financial analysis and prepare you for a successful career as a Financial Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/f3kv6d/device
Reading list
We've selected eight 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
Device-based Models with TensorFlow Lite.
Provides a practical guide to machine learning with TensorFlow, which is essential for understanding the fundamentals of deploying TensorFlow Lite models.
Provides a comprehensive guide to deep learning with Python, which is essential for understanding the fundamentals of deploying TensorFlow Lite models.
Save
Provides a comprehensive guide to Android programming, which is essential for developing Android applications that use TensorFlow Lite.
Save
Provides a comprehensive guide to iOS programming, which is essential for developing iOS applications that use TensorFlow Lite.
Provides a comprehensive guide to the Raspberry Pi, which popular embedded device for deploying TensorFlow Lite models.
Provides a comprehensive guide to machine learning with R, which can be a useful tool for understanding the fundamentals of deploying TensorFlow Lite models.
Offers a comprehensive overview of deep learning for computer vision, providing essential background knowledge for those interested in deploying computer vision models on mobile and embedded devices with TensorFlow Lite.
Save
Provides a comprehensive overview of embedded systems, which is essential for understanding the context of deploying TensorFlow Lite models on embedded devices.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f3kv6d/device
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Effort
4 weeks of study, 4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Laurence Moroney
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
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This course belongs to a
collection
called
TensorFlow: Data and Deployment. It's comprised of four courses:
- Device-based Models with TensorFlow Lite (viewing)
- Advanced Deployment Scenarios with TensorFlow
- Data Pipelines with TensorFlow Data Services
- Browser-based Models with TensorFlow.js
Good to know
Offers hands-on labs and interactive materials for a better learning experience
Builds upon TensorFlow in Practice Specialization and Deep Learning Specialization, providing a solid foundation for learners with prior knowledge
Introduces TensorFlow Lite, a technology for deploying machine learning models on mobile devices
Provides instructions on preparing models for low-powered devices and executing them on Android and iOS platforms
Covers deployment on embedded systems using TensorFlow on Raspberry Pi and microcontrollers
Assumes prior knowledge in TensorFlow and neural networks, making it suitable for experienced learners
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