人工智能和人体增强认证:Python代码 Certification in AI and Human Augmentation: Codes in Python

深度学习网络:基础和代码(A-Z包)-从人类增强，BCI到敌对量子网络
你会学到:
人工智能基础和使用Python进行人工智能深度学习的基础(Keras和TensorFlow)
用于不同应用程序的Python训练和测试的深度机器学习代码
如何在使用Keras和TensorFlow的深度学习中使用数据扩充和迁移学习技术
用于量子神经网络训练和测试的张量流量子(Python)

时长:2h 52m |视频:. MP4，1280×720 30 fps |音频:AAC，44.1 kHz，2ch |大小解压后:4.19 GB
语言：英语+中英文字幕（云桥网络 机译）

要求
不需要编程经验。你会学到你需要知道的一切
描述
人工智能是通过利用深度学习架构来转变不同领域的推动者。

该课程旨在让学生接触到与人工智能相关的前沿算法、技术和代码，尤其是深度学习例程。本课程包括下列主题的多维实现；

1.深度学习:人工智能的一个子集
2.大数据正在推动人工智能。
3.如何使用Python中的数据集对人工智能中的问题进行建模。
4.深度学习网络中的数据扩充。
5.如何在深度学习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.人工智能中超参数的反向传播和优化。
7.领先的卷积神经网络和验证指数。
8.扩展到长短期记忆的递归神经网络。
9.对绿色人工智能的理解。
10.神经网络在Keras和Pytorch中的实现以及量子机器学习介绍。
11.TensorFlow Quantum和Qiskit中与量子机器学习相关的算法。
12.使用深度学习的基于人工智能的神经疾病解决方案。
13.脑机接口和神经调节人工智能。
14、用于肿瘤诊断、预后和治疗计划的人工智能算法。
15.如何在医疗保健中建模人工智能问题。
16.区块链的人工智能和密码挖掘
17加密交易中的人工智能。
18.通过人工智能在区块链分叉。
19.使用人工智能(可互换和不可互换数字货币)的加密交易投资策略。
24.机器人学中的人工智能——完整代码的案例。
25.智能聊天机器人中的人工智能-完整代码的案例。
26.人工智能在商业分析中的影响——一个完整代码的案例。
27.媒体和创意产业中的人工智能-完整代码的案例。
28.基于人工智能的最大点击广告-完整代码的案例。
29.检测错误信息的人工智能检测。
30.使用人工智能提取时尚趋势。
31.新冠肺炎期间情绪检测的人工智能。本课程面向谁:对人工智能和python深度学习充满好奇的初学者
这门课是给谁的
对人工智能和python深度学习感兴趣的初学者

Last Update: 12/2021
Duration: 2h 52m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 4.04 GB
Genre: eLearning | Language: English

Deep Learning Networks : Basics & Codes (A-Z Package)- From Human Augmentation, BCI to Adversarial Quantum Networks
What you’ll learn:
Basics of AI and fundamentals of deep learning in AI using Python (Keras and TensorFlow)
Deep Machine Learning codes for training and testing in Python for different applications
How to use Data Augmentation and Transfer Learning Techniques in Deep Learning using Keras and TensorFlow
TensorFlow Quantum for training and testing of Quantum Neural Networks (Python)
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 deep learning routines. This course encompasses multidimensional implementations on the themes listed below;

1. Deep Learning: A subset of Artificial Intelligence
2. Big Data is Fueling AI.
3. How to model a problem in AI using datasets in Python (Keras & TensorFlow Libraries).
4. Data Augmentation in Deep Learning Networks.
5. How to use Transfer Learning in Deep Learning Networks.
6. How to use transfer learning in multiclass classification healthcare problems.
6. Backward Propagation and Optimization of hyper- parameters in AI.
7. Leading Convolutional Neural Networks (ALEXNET & INCEPTION) 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. AI based solutions for Neurological Diseases using Deep Learning.
13. AI for Brain Computer Interfacing and Neuromodulation.
14, AI algorithms for diagnosis, prognosis and treatment plans for Tumors.
15. How to model an AI problem in Healthcare.
16. AI in Block Chain 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 artificial intelligence and deep learning in python
Who this course is for
Beginner students curious about artificial intelligence and deep learning in python
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