Generative Adversarial Networks for Data Augmentation (AI)

Generative Adversarial Networks for Data Augmentation (AI)

Generative Adversarial Networks for Data Augmentation (AI)

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

Language: english

Note: 4.5/5 (77 notes) 10,272 students

Instructor(s): Junaid Zafar

Last update: 2022-06-10

What you’ll 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

 

Requirements

  • No programming experience needed. You will learn everything you need to know

 

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.

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.

 

Who this course is for

  • Beginner students curious about learning concepts of GANs, artificial intelligence and deep learning in python
  • Academic and Research Students working in the realm machine learning, deep neural networks and artificial intelligence

 

Course content

  • How Artificial Intelligence is using Big Data to model Deep Neural Networks
    • Deep Machine Learning- A subset of AI
    • Role of Big Data Computing in AI
  • Introduction to Generative Adversarial Networks
    • GANs- An Overview
  • Implementation of GANs
    • Data Augmentation: Implementation of GANs
  • Introduction to GANs
    • Generative Adversarial Networks- GANs
  • Data Augmentation using GANs
    • Data Augmentation and Features Extraction using deep CNNs
  • GAN Architectures
    • Different GAN Architectures
  • Five GANs Models one should know
    • 5 Must Know GAN Models
  • How to Model, Train and validate a deep learning Classifier in Python
    • Data Set Creation, labeling and Dynamic Programming for an AI Neural Network
  • Transfer Learning in GANs
    • How to use Transfer Learning in Healthcare Problems using Deep CNN’s
  • Training of GAN Architectures
    • Training of GAN Architectures
  • Optimizers in AI, Back-propagation and hyperparameters in AI
    • Optimizers, Backpropagation and reinforcement in Deep Learning Networks
  • GANGough for Data Augmentation
    • GANGough Applications
  • LSTMs and Tiny AI
    • Long Short Term Memory using Recurrent Neural Networks for Bio-computing
    • Tiny Artificial Intelligence for well being using Wearables and Implants
  • Modeling and Implementation of Medical Imaging Problems in Python using AI
    • Emerging AI Healthcare Landscape
    • Artificial Intelligence Nurturing Healthcare
  • Implementation of GANs for Neurodegenerative Diseases using AI
    • Implementation of GANs in AI for Neurodegenerative Disorders
  • BCI- Brain Computer Interfacing using AI
    • Brain Computer Interfacing using Python Codes
  • Implementation of GANs in Deep Learning Networks for Cancer Detection
    • Modeling of deep learning networks for Cancer Diagnosis
  • Green Artificial Intelligence
    • Green AI a primer to reduce carbon footprints
  • DarkNet Model using Vetex AI
    • DarkNet Model via Vertex AI
  • Quantum Machine Learning using Pytorch, Qiskit and TensorFLow Quantum
    • TensorFlow Quantum as a Software Framework for Quantum Machine Learning
  • Artificial Intelligence: A Case Example of Cryptocurrency
    • AI in Blockchain and Crypto-mining
  • Deep learning for implementing Forks in Block chain
    • Role of Soft and Hard forks and their impact on Digital currency
    • Fungible vs Non Fungible Block Tokens in Blockchain
  • AI for Green Cryptocurrencies
    • How to reduce carbon emissions and to generate eco friendly cryptocurrencies
  • Artificial Intelligence in Smart Chatbots
    • Architecture based NLP for Health Care Assistant
  • Misinformation Detection using Deep Learning
    • Misinformation Detection via AI
  • Smart Social Media Marketing and Business Analytics using AI
    • How to achieve maximum clicks and extract trends on social media
  • 20+ Coding Exercises & Solutions in Open CV (Computer Vision)
    • 20+ Coding Exercises & Solutions in Open CV (Computer Vision)

 

Generative Adversarial Networks for Data Augmentation (AI)

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