fullstackdeeplearning.com

Sign up for our latest in-person course! News, community, and courses for people building AI-powered products. Sign up for our latest course! Building an AI-powered product is much more than just training a model or writing a prompt. The Full Stack brings people together to learn and share best practices across the entire lifecycle of an AI-powered product: from defining the problem and picking a GPU or foundation model to production deployment and continual learning to user experience design. Get up to speed on the latest in AI-powered apps with the new Large Language Models Bootcamp. Learn best practices and tools for building applications powered by LLMs. Cover the full stack from prompt engineering and LLMops to user experience design. Build an AI-powered application from the ground up in our Deep Learning Course. You've trained your first (or 100th) model, and you're ready to take your skills to the next level. Join thousands from UC Berkeley, University of Washington, and all over the world and learn best practices for building AI-powered products from scratch with deep neural networks. We are excited to share this course with you for free. We have more upcoming great content. Subscribe to stay up to date as we release it. Enter We take your privacy and attention very seriously and will never spam you. I am already a subscriber --- Skip to content Sign up for our latest in-person course! Full Stack LLM Bootcamp 🚀 Full Stack LLM Bootcamp 🚀 Learn best practices and tools for building LLM-powered apps Cover the full stack from prompt engineering to user-centered design Get up to speed on the state-of-the-art Access the materials! Testimonials Why The way AI-powered apps are built has changed: Before LLMs, an idea would bottleneck on training models from scratch, and then it'd bottleneck again on scalable deployment. Now, a compelling MVP based on pretrained LLM models and APIs can be configured and serving users in an hour. An entirely new ecosystem of techniques, tools, and tool vendors is forming around LLMs. Even ML veterans are scrambling to orient themselves to what is now possible and figure out the most productive techniques and tools. What We put together a two-day program based on emerging best practices and the latest research results to help you make the transition to building LLM apps with confidence. We ran that program as an in-person bootcamp in San Francisco in April 2023. Now, we're releasing the recorded lectures, for free! ✨ Learn to Spell: Prompt Engineering and Other Magic 🏎️ LLMOps: Deployment and Learning in Production 🤷 UX for Language User Interfaces 🔨 Augmented Language Models 🚀 Launch an LLM App in One Hour 🔮 What's Next? 🗿 LLM Foundations 👷‍♂️ askFSDL Walkthrough What do I need to know already? The lectures aim to get anyone with experience programming in Python ready to start building applications that use LLMs. Experience with at least one of machine learning, frontend, or backend will be very helpful. Who We are Full Stack Deep Learning. We're a team of UC Berkeley PhD alumni with years of industry experience who are passionate about teaching people how to make deep neural networks work in the real world. Since 2018, we have taught in-person bootcamps, online multi-week cohorts, and official semester-long courses at top universities. Instructor Team Charles Frye educates people in AI. He has worked on AI/ML tooling with Weights & Biases and Gantry since getting a PhD in Theoretical Neuroscience at UC Berkeley. Sergey Karayev builds AI-powered products as Co-founder of Volition. He co-founded Gradescope after getting a PhD in AI at UC Berkeley. Josh Tobin builds tooling for AI products as Co-founder and CEO of Gantry. He worked as a Research Scientist at OpenAI and received a PhD in AI at UC Berkeley. If you have any questions about the bootcamp materials, contact admin @ fullstackdeeplearning.com. We are excited to share this course with you for free. We have more upcoming great content. Subscribe to stay up to date as we release it. Enter We take your privacy and attention very seriously and will never spam you. I am already a subscriber --- Skip to content Sign up for our latest in-person course! Full Stack Deep Learning Courses The Full Stack Deep Learning course started in 2018, as a three-day bootcamp hosted on Berkeley campus. Since then, we've hosted several in-person bootcamps, online courses, and official university courses. Looking for the most recent FSDL materials? You can find them here. Testimonials Past Iterations FSDL 2022 (Online): A fully online course, taught via YouTube, Crowdcast, and Discord. FSDL 2021 (Online): Contemporaneous with the Berkeley course, we taught an online cohort course. FSDL 2021 (Berkeley): Taught as a UC Berkeley undergrad course CS194-080 in Spring 2021 FSDL 2020 (UW): Taught as University of Washington Professional Master's Program course CSEP 590C in Spring 2020 FSDL 2019 (Online): Materials from the November 2019 bootcamp held on Berkeley campus organized in a nice online format. FSDL 2019 (Bootcamp): Raw materials from the March 2019 bootcamp, held on Berkeley campus. FSDL 2018 (Bootcamp): Our first bootcamp, held on Berkeley campus in August 2018 We are excited to share this course with you for free. We have more upcoming great content. Subscribe to stay up to date as we release it. Enter We take your privacy and attention very seriously and will never spam you. I am already a subscriber --- Skip to content Sign up for our latest in-person course! The Full Stack Blog Call for posts! We're just getting started with blogging, as we branch out from courses and live events. Contact us via email (team at fullstackdeeplearning dot com), via Twitter DM, or message charles_irl on Discord if you're interested in contributing! RWKV, Explained Charles Frye · 2023-07-25 #llms #rwkv #code #notebook A step-by-step explanation of the RWKV architecture via typed PyTorch code. Vanilla GPT-3 quality from an open source model on a single machine: GLM-130B Charles Frye · 2023-01-12 #model-serving #gpus #nlp #llms Notes from deploying GLM-130B, a large language model from Tsinghua KEG 1 Total 2 posts. We are excited to share this course with you for free. We have more upcoming great content. Subscribe to stay up to date as we release it. Enter We take your privacy and attention very seriously and will never spam you. I am already a subscriber