Deep Learning via GANs: Healthcare, Conv AI Applications

人工智能是通过利用深度学习架构改变不同领域的推动者。

该课程旨在让学生接触与人工智能相关的前沿算法、技术和代码；特别是用于深度学习程序中数据创建的生成对抗网络。本课程包含以下主题的多维实施；

1. 深度学习：混合人工智能的一个子集
2. 大数据正在推动应用人工智能。
3. 如何使用 Python 中的数据集对 AI 中的问题进行建模（Keras 和 TensorFlow 库）。
4. 在混合深度学习网络中使用 GAN 进行数据增强。
5. 如何在混合 GAN 网络中使用迁移学习。
6. 如何在多类分类医疗保健问题中使用迁移学习。
6. AI GAN 中超参数的反向传播和优化。
7. 使用 GAN 和验证指标的领先卷积神经网络 (ALEXNET & INCEPTION)。
8. 扩展到长短期记忆的循环神经网络。
9. 对绿色人工智能的理解。
10. Keras 和 Pytorch 中神经网络的实现以及量子机器学习的介绍。
11. TensorFlow Quantum 和 Qiskit 中与量子机器学习相关的算法。
12. 使用深度学习治疗神经系统疾病的 GAN。
13. 用于脑机接口和神经调节的 GAN。
14，基于 GAN 的人工智能算法，用于肿瘤的诊断、预后和治疗计划。
15. 如何在医疗保健中使用 GAN 对 AI 问题进行建模。
16. 人工智能；在区块链和加密货币挖矿
中 17 人工智能；在加密货币交易中。
18. 通过人工智能在区块链中分叉。
19. 使用 AI（可替代和不可替代的数字货币）进行加密交易的投资策略。
24. 机器人中的人工智能——一个完整代码的案例。
25. 智能聊天机器人中的人工智能——一个完整代码的案例。
26. AI 在业务分析中的影响- 一个完整代码的案例。
27. AI in media and creative industry – 一个完整代码的案例。
28. 基于 AI 的广告以获得最大点击量 – 带有完整代码的案例示例。
29. 用于检测错误信息检测的人工智能。
30. 使用人工智能提取时尚趋势。
31. AI；用于 Covid-19 期间的情绪检测。
本课程适用对象：对学习 GAN、人工智能和 Python 深度学习概念感兴趣的初学者 从事机器学习、深度神经网络和人工智能领域工作的学术和研究人员

Generative Artificial Intelligence: 25+ Coding Solutions via AlexNet, ResNet & Inception Models & their implentations

Instructors: Junaid Zafar
24 sections • 28 lectures • 3h 8m
Video: MP4 1280×720 44 KHz | English + Sub
Updated 4/2022 | Size: 3.7 GB

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 pythonAcademic and Research Students working in the realm machine learning, deep neural networks and artificial intelligence