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所在平台: Udemy |
课程主页: https://www.udemy.com/course/pytorch-deep-learning/
课程评论：没有评论
课程名称：PyTorch：深度学习与人工智能 课程概述： 想知道像OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等AI技术是如何工作的？在本课程中，您将学习这些突破性应用的基础知识。本课程聚焦于使用PyTorch进行深度学习和人工智能，尽管Google的深度学习库TensorFlow近年来非常流行，但PyTorch一直是全球专业人士和研究人员的首选。 本课程将讨论以下问题：TensorFlow的流行是否仅仅因为Google的品牌和有效的市场营销？其版本更新中有何变化？PyTorch是由另一互联网巨头Facebook（特别是Facebook人工智能研究实验室FAIR）支持的，正是这一点让它成为一个受欢迎的深度学习库。 课程适合初学者至专家级学生。即使您之前已经学习了我的其他课程，这里仍会有新的项目和实践，如时间序列预测和股票预测。 课程内容包括： - 主要深度学习架构，如深度神经网络、卷积神经网络和递归神经网络 - 自然语言处理（NLP）、推荐系统、迁移学习、生成对抗网络（GANs）和深度强化学习 - 理论与实践结合，让学生能快速上手，更关注PyTorch库的应用，而非数学推导 这门课程特别强调实践技能，每一行代码都有详细解释，突出动手能力，而不是仅仅用理论讲解。如果您正在寻找一个理论密集型的课程，那么这门课可能不适合您。 期待在课堂上见到您！
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Welcome to PyTorch: Deep Learning and Artificial Intelligence!Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.Deep Learning has been responsible for some amazing achievements recently, such as:Generating beautiful, photo-realistic images of people and things that never existed (GANs)Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)Self-driving cars (Computer Vision)Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)This course is for beginner-level students all the way up to expert-level students. How can this be?If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).Current projects include:Natural Language Processing (NLP)Recommender SystemsTransfer Learning for Computer VisionGenerative Adversarial Networks (GANs)Deep Reinforcement Learning Stock Trading BotEven if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses PyTorch, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).I'm taking the approach that even if you are not 100% comfortable with the mathematical concepts, you can still do this! In this course, we focus more on the PyTorch library, rather than deriving any mathematical equations. I have tons of courses for that already, so there is no need to repeat that here.Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.Thanks for reading, and I'll see you in class!WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
