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所在平台: Udemy |
课程主页: https://www.udemy.com/course/generative-adversarial-networks/
课程评论：没有评论
**课程名称：** 机器学习：生成对抗网络 (GANs) **课程概述：** 本课程将深入探讨生成模型，特别是生成对抗网络 (GANs) 的强大潜力。课程将首先直观地解释 GANs 的工作原理，然后深入剖析 Ian J. Goodfellow 等人于 2014 年发表的开创性论文，从数学层面揭示其运作机制。 学员将在此基础上，使用 Python 语言和 PyTorch 框架，在约 100 行代码内从零开始实现生成器、判别器以及论文中的伪代码，从而训练出能够生成与真实图像无法区分的合成图像。 本课程秉持“实践出真知”的理念，旨在为学员打下坚实基础，助力他们在机器学习、PyTorch 和各类生成模型（如 GANs、变分自排编码器 VAEs、归一化流 Normalizing Flows、扩散模型 Diffusion Models 等）领域继续深造。 **课程收益：** * **掌握 PyTorch 框架：** 能够运用 PyTorch 实现和优化神经网络。 * **了解生成模型应用：** 熟悉生成模型在学术研究和工业界的实际应用。 * **理解 GANs 原理：** 掌握 GANs 的直观和数学层面的工作原理。 * **数据生成能力：** 能够生成逼真的合成数据，如图像。 * **论文解读与实现：** 具备解读和实现科研论文的能力。 * **AI 解决方案开发：** 能够使用 Python 和 PyTorch 构建人工智能解决方案。 **适合人群：** * 希望入门生成模型领域的学习者。 * 希望深入学习 PyTorch 框架的学习者。 * 希望提升机器学习技能至中级水平的学习者。 **核心内容：** * PyTorch 框架 * 生成模型的应用 * GANs 的直观解释 * GANs 的数学原理 * 合成数据集的生成 * 科研论文的实现 Jump into the world of generative models now!
In this crash course, we will discuss the opportunities that generative models offer, and more specifically Generative Adversarial Networks (GANs). I will explain how GANs work intuitively, and then we will dive into the paper that introduced them in 2014 (Ian J. Goodfellow et al.). You will therefore understand how they work in a mathematical way, which will give you the foundation to implement your first GAN from scratch. We will implement in approximately 100 lines of code a generator, a discriminator and the pseudo-code described in the paper in order to train them. We will use the Python programming language and the PyTorch framework. After training, the generator will allow us to generate synthetic images that are indistinguishable from real images.I believe that a concept is learned by doing and this crash course aims to give you the necessary basis to continue your learning of Machine Learning, PyTorch and generative models (GANS, Variational Autoencoders, Normalizing Flows, Diffusion Models,...). At the end of this course, the participant will be able to use Python (and more particularly the PyTorch framework) to implement scientific papers and artificial intelligence solutions. This course is also intended to be a stepping stone in your learning of generative models. Beyond GANs, this course is also a general introduction to the PyTorch framework and an intermediate level Machine learning course. Concepts covered: The PyTorch framework in order to implement and optimize neural networks. The use of generative models in the research and industrial world. GANs in an intuitive way. GANs in a mathematical way. The generation of synthetic data (such as images). The implementation of a scientific paper. Don't wait any longer before jumping into the world of generative models!
