[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-289939-105":59,"doc-detail-289939-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","dat-494-advanced-deep-learning-course-flyer","DAT 494 - Advanced Deep Learning - Course Flyer","","DAT 494 Advanced Deep Learning is an instructor-led course by Steffen Eikenberry, scheduled TTh 10:30–11:45 AM in Tempe DH 210. The course explores how modern AI models work, focusing on practical building and training: constructing a GPT-2 clone chatbot, using open-source models with Retrieval Augmented Generation (RAG), implementing text-to-image systems with diffusion models, and training agents via deep reinforcement learning in simulated environments. Topics include transformers, GANs, VAEs, diffusion, Gymnasium, deep Q learning, temporal difference learning, and world models, with an emphasis on Python and PyTorch.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/dat-494-advanced-deep-learning-course-flyer/289939/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/dat-494-advanced-deep-learning-course-flyer/289939.png","ImageObject",300,407,{"name":92,"@type":93},"Bill Black","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-12","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What will students build during DAT 494?","Question",{"text":113,"@type":114},"Students will build and train a GPT-2 clone chatbot, create more advanced chatbots with Retrieval Augmented Generation (RAG), implement text-to-image generators using diffusion models, and train agents using deep reinforcement learning in simulated environments.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which model families and learning methods are covered?",{"text":118,"@type":114},"The course covers transformer-based autoregressive language models, GANs, VAEs, and diffusion models, along with reinforcement learning topics such as Gymnasium, temporal difference learning, deep Q learning, and world models.",{"name":120,"@type":111,"acceptedAnswer":121},"What programming tools does the course emphasize?",{"text":122,"@type":114},"The course emphasizes building models in Python using PyTorch, while also covering workflows using existing high-level libraries and transfer learning with pre-trained models for generative and reinforcement AI.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},289939,1789638588,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81},24189269381491,"https://ap-avatar.wpscdn.com/avatar/160000cf11732dd8392?x-image-process=image/resize,m_fixed,w_180,h_180&k=1788146458752108895","DAT 494: Advanced Deep Learning  \nInstructor: Steffen Eikenberry  \nEmail: [seikenbe@asu.edu](seikenbe@asu.edu)  \nLogistics: TTh 10:30-11:45 AM, Tempe DH 210  \nCurious how modern AI models, including language models like ChatGPT, image generators, and AI agents, work? In this course we will learn to, among other things:  \n• Build and train a GPT-2 clone (early version of ChatGPT) from the ground up, and turn it into an interactive Chatbot  \n• Use open-source models to power more advanced Chatbots, and enhance these with Retrieval Augmented Generation (RAG)  \n• Use open-source diffusion models to rapidly implement your own text-to-image generators  \n• Train agents to navigate multiple simulated environments using deep reinforcement learning  \nCourse Description and Topics: Deep learning with artificial neural nets underpins all modern AI, such as large language models and image and video generators. This course presents multiple advanced topics in some depth, including advanced convolutional nets, generative AI, and reinforcement learning (RL) . Generative models discussed include transformer-based autoregressive language models, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. Reinforcement learning topics include Gymnasium (OpenAI Gym), temporal difference learning, deep Q learning, RL libraries, and world models. Emphasis will be placed on building models in Python using PyTorch, but implementing workflows using existing high-level libraries and transfer learning with pre-trained models will also be covered, as will AI agents and retrieval augmented generation (RAG). The course will assume familiarity with Python and at least some previous experience with machine learning and PyTorch.  \nSuggested Prerequisites: Familiarity with Python and basic neural nets is required. DAT 494: Deep Learning Structures, or equivalent, may be helpful but is not required.  \nCore Objectives:  \n• Describe core elements of modern AI architectures, including embedding, attention mechanisms, and latent space  \n• Describe and implement (simplified versions of) major generative AI model architectures in Python with PyTorch  \n• Describe and implement fundamental deep reinforcement learning algorithms in Python  \n• Use high-level libraries for generative and reinforcement AI workflows","cbCaimZBzLi4S1yB","https://ap.wps.com/l/cbCaimZBzLi4S1yB","pdf",201259,"English","# Overview\n## Course Logistics\n## Key Learning Outcomes\n# Core Topics\n## Generative AI Models\n## Reinforcement Learning\n## Tools and Frameworks\n# Prerequisites","[{\"question\":\"What will students build during DAT 494?\",\"answer\":\"Students will build and train a GPT-2 clone chatbot, create more advanced chatbots with Retrieval Augmented Generation (RAG), implement text-to-image generators using diffusion models, and train agents using deep reinforcement learning in simulated environments.\"},{\"question\":\"Which model families and learning methods are covered?\",\"answer\":\"The course covers transformer-based autoregressive language models, GANs, VAEs, and diffusion models, along with reinforcement learning topics such as Gymnasium, temporal difference learning, deep Q learning, and world models.\"},{\"question\":\"What programming tools does the course emphasize?\",\"answer\":\"The course emphasizes building models in Python using PyTorch, while also covering workflows using existing high-level libraries and transfer learning with pre-trained models for generative and reinforcement AI.\"}]","DAT 494 - Advanced Deep Learning - Course Flyer | PDF"]