Description
Buy Ritesh Kanjee – Project E.D.I.T.H. AI Smart Glasses Course at esyGB. You will have immediate access to the digital downloads in your account or your order email.
Welcome to the Project E.D.I.T.H. AI Course
Project E.D.I.T.H. is a course that is designed to teach you Artificial Intelligence by prototyping the AI Smart Glasses that Tony Stark handed down to Peter Parker. Put your self in the shoes of a superhero as we prototype Stark-Tech.
There are 7 Phases that teach you how to build the AI behind the E.D.I.T.H. Glasses. Let’s take a look at all the Phases and what you will learn in this course:
Phase 1 – Face Detection
Learn the the essentials of Project E.D.I.T.H., Here we start with face detection to recognize one face and display the face identification.
Phase 2 – Facial Recognition
Multiple Face Recognition is one of the key functionalities that we seen in Far from Home. We will working in Unity and OpenCV to be able to identify various faces.
Phase 3 – Object Detection
We want to scan for objects and for the AI to tell us what we are looking at, this can be achieved with either a MobileNet Object Detection or a TensorFlow Image Classifier
Phase 4 – AI Voice Assistant
In this phase we can implement Voice Commands similar to Google Assistant or Alexa to execute a drone strike…Just kidding, to scan for objects and faces or interact with a design interface.
Phase 5 – AI + IoT + AR
The IoT Solution – So this entails switching on/off our IoT devices and giving us visual feedback on our device if possible.
Phase 6 – AI on Android
Having all this power on a gaming PC is really nice, but we will need eventually make it mobile. This lecture will teach you how to port your AI Object Detection models for inference on a mobile device.
Phase 7 – AI on Smart Glasses
Once we have it running on mobile devices, can we turn it into wearable tech such as the nReal or Project North Star smart glasses. This is the platform where we can test and develop our EDITH AI Smart Glasses.
Your Instructor
I am the founder of Augmented Startups and I also hold a Masters Degree in Electronic Engineering. With over 63’000+ students on Augmented AI Bootcamp, and over 79’000 subscribers on YouTube, I teach the latest topics on Artificial Intelligence and Augmented Reality I will act as your mentor through helping you build or grow your expertise, we look forward to having you!
Bonuses:
Bonus 1 – Bonus AI Lectures
Bonus 2 – Accelerate Deep Learning on a Raspberry Pi Course [FREE]
Get FREE access to the course that will show you how to accelerate your AI Object Detection models 5X faster on a Raspberry Pi 3, using Intel Movidius for Deep Learning.
Bonus 3 – Private Community Mentorship
Instant access to the private Facebook group of other course creators to receive professional and peer mentoring.
Get a Career Boost with a Certificate of Completion
Upon completing 100% of this course, you will be emailed a certificate of completion. You can show it as proof of your expertise and that you have completed a certain number of hours of instruction.
Lifetime Access
Instant access to ALL the content FOR LIFE! And the content just keeps coming as Ritesh and Geeky Bee AI are constantly learning and sharing!
Money-Back Guarantee
Let me help you get fast results. Enroll now, by clicking the button and let us show you how to Develop your own E.D.I.T.H. AI Smart Glasses. Build apps in AI, IoT, AR, VR and Android.
Course Curriculum
Project E.D.I.T.H. AI Smart Glasses
Introduction to the Course
Introduction [COMING SOON]
Course Requirements
How to Join the Private FB Group
Project EDITH Smart Glasses Core Module
Phase 1 – Face Detection (9:36)
Phase 2 – Multiple Face Recognition (17:09)
Phase 3 – Object Detection (19:49)
Phase 4 – J.A.R.V.I.S. and E.D.I.T.H. AI Voice Assistant (18:18)
Phase 5 – Fuse AI with IoT with AR (AI+IoT+AR) (11:53)
Phase 6 – Android AI Solution (10:00)
Phase 7 – nReal Glasses Intergration [Coming Soon]
Conclusion
Where to from Here?
Bonus Section
Artificial Neural Networks – Theory (18:30)
Convolutional Neural Networks – Theory (11:17)
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