Description

Augmented Reality projects with ARCore 1.6

Augmented Reality projects with ARCore 1.6
.MP4, AVC, 380 kbps, 1920×10800 | English, AAC, 160 kbps, 2 Ch | 2h 35m | 870 MB Instructor: Bharath Nagarajan

Create cutting-edge AR and MR projects showcasing face detection, location-based AR, and gesture recognition using Google’s ARCore 1.6

Augmented reality is the next big thing. By 2022, it is expected to have a market size of $200 billion. With its huge potential and demand, it has soon found many applications and developers need to become proficient in the field. Google’s ARCore might be the long-sought catalyst that augmented reality is looking for.

This course starts you off with augmented reality project development with ARCore 1.4. You will use each of its core features to create your own AR projects. You will first build an AR greeting card application that adds life to characters and images. You will also build and project life-size 3D furniture models like those in the IKEA furniture AR app. You’ll design a variety of face masks that you see on Snapchat and Instagram filters. You’ll also build location-based AR applications identical to Pok?mon Go with ARCore. You’ll find yourself in a shared AR experience in real-time with a projectile shooter multiplayer game. Finally you will interact with virtual objects in the real world by using gesture controls without the need for hardware such as the Xbox Kinect.

Are you tired of courses based on over-used, outdated data sets?

Yes? Well then you’re in for a treat.

Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:

• Artificial Neural Networks to solve a Customer Churn problem
• Convolutional Neural Networks for Image Recognition
• Recurrent Neural Networks to predict Stock Prices
• Self-Organizing Maps to investigate Fraud
• Boltzmann Machines to create a Recomender System
• Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize

*Stacked Autoencoders is a brand new technique in Deep Learning which didn’t even exist a couple of years ago. We haven’t seen this method explained anywhere else in sufficient depth.

4. HANDS-ON CODING 

In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.