Generative Adversarial Networks for Data Augmentation (AI)


GANs for Artificial Intelligence implementations: Basics and 25+ Coding Solutions via AlexNet, ResNet & Inception Models

What you will learn

Learn to model Artificial Intelligence using GANs: AlexNet, Inception to ResNet architectures for Computer Vision and Bioinformatics

GAN Architectures- Introduction and Different GAN Methods

Data Augmentations using GANs

TensorFlow Quantum for training and testing of Hybrid Quantum Neural Networks for Computer Vision in Healthcare(Python)

Applied Artificial Intelligence: Concept to diverse practical implications

Applied AI nurturing healthcare: Code Examples using Python programming

20+ Coding Exercises and Solutions in Open CV for Computer Vision

Implementations of Transfer Learning and GANs in AlexNet, Inception & ResNet for various real life AI centric applications

How to build and implement leading AI architectures in Keras and TensorFlow Quantum with emphasis on medical computer vision

Description

AI is an enabler in transforming diverse realms by exploiting deep learning architectures.

The course aims to expose students to cutting-edge algorithms, techniques, and codes related to AI and particularly the Generative Adversarial Networks used for data creation in deep learning routines. This course encompasses multidimensional implementations of the themes listed below;

1. Deep Learning: A subset of Hybrid Artificial Intelligence

2. Big Data is Fueling Applied AI.

3. How to model a problem in AI using datasets in Python (Keras & TensorFlow Libraries).

4. Data Augmentation using GANs in Hybrid Deep Learning Networks.

5. How to use Transfer Learning in Hybrid GAN Networks.

6. How to use transfer learning in multiclass classification healthcare problems.

6. Backward Propagation and Optimization of hyper-parameters in AI GANs.

7. Leading Convolutional Neural Networks (ALEXNET & INCEPTION) using GANs and validation indices.

8. Recurrent Neural Networks extending to Long Short Term Memory.

9. An understanding of Green AI.

10. Implementations of Neural Networks in Keras and Pytorch and introduction to Quantum Machine Learning.

11. Algorithms related to Quantum Machine Learning in TensorFlow Quantum and Qiskit.

12. GANs for Neurological Diseases using Deep Learning.

13. GANs for Brain-Computer Interfacing and Neuromodulation.


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14, GAN based AI algorithms for diagnosis, prognosis, and treatment plans for Tumors.

15. How to model an AI problem using GAN in Healthcare.

16. AI in BlockChain and Crypto mining

17 AI in Crypto trading.

18. Forks in Block Chain via AI.

19. Investment Strategies in Crypto- trade using AI (Fungible and Non- Fungible Digital Currencies).

24. Artificial Intelligence in Robotics- A case example with complete code.

25. Artificial Intelligence in Smart Chatbots- A case example with complete code.

26. Impact of AI in business analytics- A case example with complete code.

27. AI in media and creative industries- A case example with complete code.

28. AI based advertisements for maximum clicks- A case example with complete code.

29. AI for the detection of Misinformation Detection.

30. Extraction of Fashion Trends using AI.

31. AI for emotion detections during Covid- 19.

English
language

Content

AI Healthcare through Big Data and Deep Neural Networks

Artificial Intelligence Nurturing Healthcare
Deep Machine Learning- A subset of AI
Role of Big Data Computing in AI
Emerging AI Healthcare Landscape
Neural Networks in AI for Neurodegenerative Disorders

Artificial Intelligence in Behavioral and Mental Health Care

AI for Depressive Disorders and Brain Computer Interfacing
Medical Imaging using Neural Networks for Cancer Diagnosis and Prognosis

How to Model, Train and validate an AI Healthcare Problem

Data Set Creation, labeling and Dynamic Programming for an AI Neural Network
Data Augmentation and Features Extraction using deep CNNs
How to use Transfer Learning in Healthcare Problems using Deep CNN’s

Optimizers in AI and Back-propagation

Optimizers, Backpropagation and reinforcement in Deep Learning Networks
Long Short Term Memory using Recurrent Neural Networks for Bio-computing
Tiny Artificial Intelligence for well being using Wearables and Implants

Quantum Machine Learning using Pytorch, Qiskit and TensorFLow Quantum

TensorFlow Quantum as a Software Framework for Quantum Machine Learning

Green Artificial Intelligence

Green AI a primer to reduce carbon footprints

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