Course Summary
Learn how to build device-based models using TensorFlow, a powerful tool for deep learning. In this course, you will learn how to develop models that can be deployed on mobile and embedded devices.Key Learning Points
- Develop deep learning models for mobile and embedded devices
- Learn how to optimize models for performance and energy efficiency
- Explore TensorFlow tools and APIs for developing device-based models
Job Positions & Salaries of people who have taken this course might have
- USA: $118,000
- India: ₹1,000,000
- Spain: €40,000
- USA: $118,000
- India: ₹1,000,000
- Spain: €40,000
- USA: $93,000
- India: ₹700,000
- Spain: €30,000
- USA: $118,000
- India: ₹1,000,000
- Spain: €40,000
- USA: $93,000
- India: ₹700,000
- Spain: €30,000
- USA: $102,000
- India: ₹800,000
- Spain: €35,000
Related Topics for further study
Learning Outcomes
- Develop models that can be deployed on mobile and embedded devices
- Optimize models for performance and energy efficiency
- Gain practical experience with TensorFlow tools and APIs
Prerequisites or good to have knowledge before taking this course
- Prior experience with machine learning and TensorFlow
- Familiarity with mobile and embedded devices
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Advanced Machine Learning with TensorFlow on Google Cloud Platform
- TensorFlow: Data and Deployment
Related Education Paths
Notable People in This Field
- Andrew Ng
- Ian Goodfellow
Related Books
Description
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.
Knowledge
- Prepare models for battery-operated devices
- Execute models on Android and iOS platforms
- Deploy models on embedded systems like Raspberry Pi and microcontrollers
Outline
- Device-based models with TensorFlow Lite
- Introduction, A conversation with Andrew Ng
- A few words from Laurence
- Features and components of mobile AI
- Architecture and performance
- Optimization Techniques
- Saving, converting, and optimizing a model
- Examples
- Quantization
- TF-Select
- Paths in Optimization
- Running the models
- Transfer learning
- Converting a model to TFLite
- Transfer learning with TFLite
- Prerequisites
- Downloading the Ungraded Labs and Programming Assignments
- GPU delegates
- Learn about supported ops and TF-Select
- Week 1 Wrap up
- Exercise Description
- Running a TF model in an Android App
- Introduction, A conversation with Andrew
- Installation and resources
- Architecture of a model
- Initializing the Interpreter
- Preparing the Input
- Inference and results
- Code walkthrough
- Run the App
- Classifying camera images
- Initialize and prepare input
- Demo of camera image classifier
- Initialize model and prepare inputs
- Inference and results
- Demo of the object detection App
- Code for the inference and results
- Android fundamentals and installation
- Week 2 Wrap up
- Description
- Building the TensorFLow model on IOS
- Introduction, A conversation with Andrew Ng
- A few words from Laurence
- What is Swift?
- TensorFlowLiteSwift
- Cats vs Dogs App
- Taking the initial steps
- Scaling the image
- More steps in the process
- Looking at the App in Xcode
- What have we done so far and how do we continue?
- Using the App
- App architecture
- Model details
- Initial steps
- Final steps
- Looking at the code for the image classification App
- Object classification intro
- TFL detect App
- App architecture
- Initial steps
- Final steps
- Looking at the code for the object detection model
- Important links
- Apple’s developer's siteÂ
- Apple's API
- More details
- Camera related functionalities
- The Coco dataset
- Week 3 Wrap up
- Description
- TensorFlow Lite on devices
- Introduction, A conversation with Andrew Ng
- A few words from Laurence
- Devices
- Starting to work on a Raspberry Pi
- How do we start?
- Image classification
- The 4 step process
- Object detection
- Back to the 4 step process
- Raspberry Pi demo
- Microcontrollers
- Closing words by Laurence
- A conversation with Andrew Ng
- Edge TPU models
- Options to choose from
- Pre optimized mobileNet
- Object detection model trained on the coco
- Suggested links
- Description
- Wrap up
Summary of User Reviews
Device-based models with TensorFlow is a highly recommended course for those looking to gain a deeper understanding of TensorFlow and its applications. Students have praised the course for its comprehensive content and engaging delivery.Key Aspect Users Liked About This Course
The course provides a detailed understanding of TensorFlow and its applications.Pros from User Reviews
- The course covers a wide range of topics related to device-based models and TensorFlow.
- The course is well-structured and easy to follow.
- The instructors are knowledgeable and engaging, making the course enjoyable to complete.
- The course includes practical exercises that help to reinforce the concepts learned.
- The course provides a wealth of resources for further learning and exploration.
Cons from User Reviews
- Some users found the course to be too technical and challenging.
- The course may not be suitable for beginners with no prior knowledge of TensorFlow.
- The course may require a significant time commitment to complete.
- Some users found the course to be too focused on theory and not enough on practical applications.
- The course may be too advanced for those with limited experience in machine learning.
Laurence Moroney
4.7 Raiting
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