Post Graduate Certificate in Deep Learning for Computer Vision and Extended Reality
Offered By: Indian Institute of Technology Guwahati via Coursera
Course Description
Overview
In this programme, you will first build a strong foundation in deep learning, computer vision, and extended reality. You will then learn to develop and deploy full-fledged deep learning-based models and applications.
As you advance through the programme, you’ll learn how to:
- Navigate deep learning and machine learning models.
- Build basic and advanced deep learning models such as MLP, Encoder-Decoder, GAN, and more..
- Build deep learning based applications from scratch using Python and other software that includes Unity game engine and XR SDKs.
Working professionals, as well as undergraduates and postgraduate students, looking to specialise in the fields of deep learning, computer vision, and extended reality will find this programme useful.
As you advance through the programme, you’ll learn how to:
- Navigate deep learning and machine learning models.
- Build basic and advanced deep learning models such as MLP, Encoder-Decoder, GAN, and more..
- Build deep learning based applications from scratch using Python and other software that includes Unity game engine and XR SDKs.
Working professionals, as well as undergraduates and postgraduate students, looking to specialise in the fields of deep learning, computer vision, and extended reality will find this programme useful.
Syllabus
Course 1: Neural Network with TensorFlow/Keras
- In this course, you will be introduced to the field of machine learning and neural networks. You will learn about feed-forward neural network/multi-layer perceptrons and implement MLP in TensorFlow and Keras for deep learning applications.
Course 2: Fundamentals of Deep Learning
- In this course, you will learn three neural network models— RNN/LSTM, CNN, and Encoder-Decoder—which are the building blocks of various deep learning models, followed by their implementation in TensorFlow for various applications like representation, classification, and regression.
Course 3: Deep Learning - Advanced
- In this course, you will learn various advanced deep learning models such as attention, generative adversarial network (GAN), N-shot learning, multi-tasks learning and transfer learning in detail, followed by the implementation of these models with various real-world use cases.
Course 4: Introduction to Extended Reality (XR)
- In this course, you will be introduced to the field of extended reality, which includes virtual reality, augmented reality and mixed reality. You will learn about the field of XR and how human sensory organs play an important role in the development of successful XR systems. You will also get an in-depth introduction to XR hardware and software and the various methods of interaction with XR environments.
Course 5: XR Foundation
- In this course, you will gain an in-depth understanding of the XR system development process. You will first learn about the stages to build XR systems. You will then be introduced to fundamentals of computer graphics and computer vision for better understanding of the working of an XR system. Finally, you will get to know about the use of deep learning in the development of XR systems.
Course 6: Design & Implementation of AR/VR Systems
- In this course, you will learn about the Unity engine in detail along with two SDKs - one for VR system development and the other for AR system development. You will also learn to test and deploy the systems on desktops, laptops, and mobile platforms.
- In this course, you will be introduced to the field of machine learning and neural networks. You will learn about feed-forward neural network/multi-layer perceptrons and implement MLP in TensorFlow and Keras for deep learning applications.
Course 2: Fundamentals of Deep Learning
- In this course, you will learn three neural network models— RNN/LSTM, CNN, and Encoder-Decoder—which are the building blocks of various deep learning models, followed by their implementation in TensorFlow for various applications like representation, classification, and regression.
Course 3: Deep Learning - Advanced
- In this course, you will learn various advanced deep learning models such as attention, generative adversarial network (GAN), N-shot learning, multi-tasks learning and transfer learning in detail, followed by the implementation of these models with various real-world use cases.
Course 4: Introduction to Extended Reality (XR)
- In this course, you will be introduced to the field of extended reality, which includes virtual reality, augmented reality and mixed reality. You will learn about the field of XR and how human sensory organs play an important role in the development of successful XR systems. You will also get an in-depth introduction to XR hardware and software and the various methods of interaction with XR environments.
Course 5: XR Foundation
- In this course, you will gain an in-depth understanding of the XR system development process. You will first learn about the stages to build XR systems. You will then be introduced to fundamentals of computer graphics and computer vision for better understanding of the working of an XR system. Finally, you will get to know about the use of deep learning in the development of XR systems.
Course 6: Design & Implementation of AR/VR Systems
- In this course, you will learn about the Unity engine in detail along with two SDKs - one for VR system development and the other for AR system development. You will also learn to test and deploy the systems on desktops, laptops, and mobile platforms.
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