Generative Adversarial Networks (GANs) in Practice

深度学习是机器学习中最新和最先进的主题之一，在许多领域都有多种应用。从计算机视觉到药物发现和股市预测，它在许多领域都显示出可喜的成果。有很多关于深度学习的书籍和文章讨论了它的算法、理论和应用。此外，由于其通过部署不同数据类型解决不同问题的能力和潜力，许多研究人员和非计算机科学或相关领域的人员对在他们的项目中学习和使用深度学习架构感兴趣。

本课程向您介绍人工神经网络和深度学习的一些基础知识，然后重点介绍生成对抗网络及其应用，并提供一些编码示例以更好地理解这些概念。本课程适合机器学习领域和深度学习的新手，想学习如何使用python、TensorFlow和Keras实现深度学习算法（尤其是GAN算法）。

该课程有七章，从机器学习概念的一些基础知识开始，以 CycleGAN 的一些想法结束。每章都有一些测验和作业来测试学生的学习情况。它还为每个项目提供解决方案。

我希望这门课程的内容会受到全球本科生和研究生以及深度学习研究人员的欢迎，包括学术界和工业界的从业者。

With Introductory Review on Artificial Neural Networks and Deep Learning Algorithms and Models

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.70 GB | Duration: 7h 16m

What you’ll learn

• The fundamentals of Artificial Neural Networks (ANNs) and reviews state-of-the-art DL examples.
• The fundamental of Deep learning and the most popular algorithms.
• The most popular GAN algorithms features and requirements .
• How to implement a GAN model in PRACTICE.
• Several examples and applications of GAN.

Requirements

Probability,

Calculus,

Basic of Python, Tensor Flow, Keras, and Numpy.

Description

Deep learning is one of the most recent and advanced topics in machine learning, with several applications in many fields. It shows promising results in many areas, from computer vision to drug discovery and stock market prediction. There are many books and articles on deep learning that discuss its algorithms, theories, and applications. Also, because of its capabilities and potential in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using deep learning architectures in their projects.

This course gives you some fundamentals of artificial neural networks and deep learning and then has focused on Generative Adversarial networks and their applications with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms (especially GAN algorithms) using python, TensorFlow, and Keras.

The course has seven chapters and starts with some fundamentals in machine learning concepts and end with some idea in CycleGAN. Each chapter has some quizzes and assignments to test students learning. It also provides the solution for each project.

I would expect this course’s contents to be welcomed worldwide by undergraduate and graduate students and researchers in deep learning, including practitioners in academia and industry.

Who this course is for

• It is useful for undergraduate and graduate students, as well as practitioners in industry and academia.
• Anyone who would like to learn Neural networks, deep learning and GANs.