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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-and-chatgpt/
课程评论：没有评论
课程名称：人工智能、神经网络与ChatGPT 课程概述：人工智能（AI）已经成为一种技术革命，能够提供超越传统规则基础方法的益处。AI与神经网络可以克服复杂性，并优化通信网络、计算机图形、多媒体和语言处理系统、数据科学、导航及语音助手等多个应用的性能。本课程通过220多张信息丰富的幻灯片、两个有趣的项目和Python代码，帮助学员掌握自然智能学习系统和生成式AI的基础知识，包括全球现象ChatGPT中使用的大规模语言模型。同时，课程中还包含一个简短的测验。 学习成果： - 了解AI和机器学习及其能力 - 学习ChatGPT、Llama、Claude、Gemini、Grok和Deep Seek的设计 - 掌握神经网络的架构和实现，包括感知器和Adaline训练、反向传播、具有记忆的吸引子网络、生物和竞争学习，以及使用Kolmogorov-Arnold网络的可解释建模 - 探索使用监督、强化和无监督训练的生成式AI - 了解使用递归神经网络、卷积神经网络（如AlexNet、Unet）、马尔可夫和扩散模型以及Adam优化器的语音、图像和视频AI增强 - 研究ChatGPT的组成部分，包括分词、嵌入、编码、解码、语言及后处理 讲师简介：讲师在佐治亚理工学院工作多年，活跃于IEEE及行业会议。他培训了来自全球各公司的数百名工程师，当前研究兴趣集中在利用AI优化媒体处理（语言、语音、音频、视频）和无线系统。他是IEEE的高级会员。
Artificial intelligence (AI) has recently emerged to be a technology revolution that is able to provide benefits beyond traditional rules-based approaches. AI and neural networks are able to overcome the complexities and optimize the performance of communications networks, computer graphics, multimedia and language processing systems, data science, navigation and voice assistants, and numerous applications. Using over 220 informative slides, two interesting projects, and Python code, this course will equip participants with the foundational knowledge on the key building blocks of naturally intelligent learning systems and generative AI, including large-scale language models used in the global phenomenon ChatGPT. It also contains a short quiz.Learning OutcomesOverview of AI and machine learning, and their capabilitiesStudy the design of ChatGPT, Llama, Claude, Gemini, Grok, and Deep SeekNeural network architecture and implementation, including perceptron and adaline training, backpropagation and attractor networks with memory, biological and competitive learning, and interpretable modeling using the Kolmogorov-Arnold NetworkGenerative AI using supervised, reinforcement, unsupervised trainingSpeech, image, and video AI enhancements using recurrent neural network, convolutional neural network (AlexNet, Unet), Markov and diffusion models, and Adam optimizerChatGPT components, including tokenization, embedding, encoding, decoding, language and post processingAbout the InstructorThe instructor has worked at the Georgia Institute of Technology for many years and has been an active speaker for the IEEE and industry. He has trained hundreds of engineers from various companies around the globe. His current research interest is in optimizing media processing (language, speech, audio, video) and wireless systems using AI. He is a senior member of the IEEE.
