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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deeplearning_x/
课程评论：没有评论
课程名称：深度理解深度学习（附Python入门） 课程概览： 深度学习正在迅速主导技术发展，并且对社会产生重大影响。从自动驾驶汽车到医学诊断，从人脸识别到深度伪造，再到语言翻译和音乐生成，深度学习在现代科技的各个领域如火如荼地发展。然而，深度学习不仅仅是关于高大上的尖端应用，它正在成为机器学习、数据科学和统计学中的标准工具。小型初创公司利用深度学习进行数据挖掘和降维，政府利用其检测税收逃逸，科学家们则用它识别研究数据中的模式。可以说，深度学习现已在科技、商业和娱乐的多个领域得到应用，并且其重要性逐年上升。 课程内容： 课程的核心在于深入探讨深度学习，帮助您建立灵活、基本且持久的深度学习专业知识。课程目标是让您全面理解深度学习的基本概念，从而能够学习未来出现的新主题和趋势。与那些希望快速了解深度学习的人不同，本课程旨在帮助您真正理解深度学习的工作原理，包括选择超参数（如优化器、归一化和学习率）的时机和方法，评估深度神经网络模型性能的方法，以及如何修改和调整现有模型以解决新问题。 您将学习： - 理论：深度学习模型的构建原理 - 数学：深度学习的公式和机制 - 实现：如何在Python（使用PyTorch库）中构建深度学习模型 - 直觉：为什么选择某个超参数，如何解释正则化的效果等 - Python编程：适合所有水平的Python学习者，包括8小时以上的编码教程 - Google Colab在线工具的使用，无需下载和安装 课程特色： - 清晰易懂的深度学习概念解释，涵盖迁移学习、生成建模、卷积神经网络、前馈网络、生成对抗网络（GAN）等内容 - 对相同概念多种不同的解释，采用被验证的学习技术 - 使用图形、数字和空间可视化，提供直观理解 - 大量练习、项目、代码挑战和探索建议，通过实践学习 - 活跃的问答论坛，您可以提问、获取反馈并与社区互动 - 8小时以上的Python教程，您无需在入课前精通Python 欢迎观看课程介绍视频和免费样本视频，深入了解课程内容和我的教学风格。如果您对本课程是否适合您有疑问，欢迎在报名之前随时与我联系。 期待在课堂上见到您！ Mike
Deep learning is increasingly dominating technology and has major implications for society.From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.How does deep learning work?Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.Is it really that simple? mmm OK, it's actually a tiny bit more complicated than that ;) but that's the core idea, and everything else - literally everything else in deep learning - is just clever ways of putting together these fundamental building blocks. That doesn't mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning - I mean, really learn deep learning, not just have superficial knowledge from a youtube video - by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport!What is this course all about?Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future.Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems.You can learn everything about deep learning in this course.In this course, you will learn Theory: Why are deep learning models built the way they are? Math: What are the formulas and mechanisms of deep learning?Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)?Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc.Python: If you're completely new to Python, go through the 8+ hour coding tutorial appendix. If you're already a knowledgeable coder, then you'll still learn some new tricks and code optimizations.Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google's cloud services. No need to install anything on your computer.Unique aspects of this courseClear and comprehensible explanations of concepts in deep learning, including transfer learning, generative modeling, convolutional neural networks, feedforward networks, generative adversarial networks (GAN), and more.Several distinct explanations of the same ideas, which is a proven technique for learning.Visualizations using graphs, numbers, and spaces that provide intuition of artificial neural networks.LOTS of exercises, projects, code-challenges, suggestions for exploring the code. You learn best by doing it yourself!Active Q & A forum where you can ask questions, get feedback, and contribute to the community.8+ hour Python tutorial. That means you don't need to master Python before enrolling in this course.So what are you waiting for??Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.I hope to see you soon in the course!Mike
