Instructors: Junaid Zafar
24 sections • 28 lectures • 3h 8m
Video: MP4 1280×720 44 KHz | English + Sub
Updated 4/2022 | Size: 3.7 GB
Generative Artificial Intelligence: 25+ Coding Solutions via AlexNet, ResNet & Inception Models & their implentations
Generative Artificial Intelligence: 25+ Coding Solutions via AlexNet, ResNet & Inception Models & their implentations
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
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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 pythonAcademic and Research Students working in the realm machine learning, deep neural networks and artificial intelligence
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