Nanodegree Program
Embark on a transformative journey into Generative AI! We'll start by diving into the essentials with an introductory course, progress to mastering text generation with Large Language Models, unravel the complexities of image creation in computer vision and cap it off by bringing AI to life in real-world applications. From foundational theories to building sophisticated chatbots and AI agents, this program will empower you with job-ready skills in the exciting field of Generative AI.
Embark on a transformative journey into Generative AI! We'll start by diving into the essentials with an introductory course, progress to mastering text generation with Large Language Models, unravel the complexities of image creation in computer vision and cap it off by bringing AI to life in real-world applications. From foundational theories to building sophisticated chatbots and AI agents, this program will empower you with job-ready skills in the exciting field of Generative AI.
Intermediate
4 months
Real-world Projects
Completion Certificate
Last Updated April 25, 2024
Course 1 • 45 minutes
Welcome to Udacity! We're excited to share more about your Nanodegree program and start this journey with you!
Course 2 • 4 weeks
Dive into generative AI with this course, which explores its fundamental principles and relationship to prior artificial intelligence innovations. We will walk through popular generative models and how they work, how deep learning models are developed using tools like PyTorch and Hugging Face, and finally, how to customize pre-trained open-source models for a specific use case. In the project, you will apply a cutting-edge technique called parameter-efficient fine-tuning (PEFT), which allows for the adaptation of massive foundation models with minimal usage of computational resources.
Course 3 • 4 weeks
Dive deeper into how computers understand and create language, and learn how to build a custom chatbot using unsupervised machine learning, prompt engineering, and retrieval augmented generation. We'll start with a high-level overview of the types of LLMs, the differences between them, and how best to account for their strengths and weaknesses. Then we'll get into the internal details, including natural language processing (NLP) techniques like tokenization, as well as modern transformer architectures and attention mechanisms. Finally, we'll build a practical LLM application that combines an LLM with a custom dataset.
Course 4 • 4 weeks
Learn how computers process and understand image data, then harness the power of the latest Generative AI models to create new images.
Head of Core AI
Brian Cruz is the Head of Core AI at Samba TV, where he leads the initiative to use AI to improve the TV viewing experience. He formerly worked at Salesforce as a Machine Learning Engineer, creating models for forecasting sales revenue as part of Einstein Guidance. He has a degree in Pure Mathematics from UC Berkeley.
Research Scientist
Emily McMilin is a Senior Research Scientist and Independent Researcher working at the intersection of NLP and Causal Inference. She obtained her Ph.D. in Electrical Engineering from Stanford University and prior to that an M.Sc. from University of Victoria, and a B.Sc. from Stanford in Symbolic Systems.
Machine Learning Engineer
Victor Geislinger is a machine learning engineer and is dedicated to sharing his knowledge with others. Victor recently joined Google as a software engineer focused on AI/ML but has been programming and educating others for over a decade since studying physics and math at the University of California, Santa Cruz.
Chief Scientist, Reasonly AI
Jason has developed deep learning algorithms and AI applications at Lyft self-driving, Spotify and Google DeepMind. Formerly a Stanford Online and UN keynote speaker, he's earned a M.S. in Machine Learning from Georgia Tech and coauthored NLP and computer vision papers with MIT.
Principal AI Researcher
Erick Galinkin is a hacker and computer scientist, leading research at the intersection of security and artificial intelligence at Rapid7. He has spoken at numerous industry and academic conferences on topics ranging from malware development to game theory in security.
Principal Data Scientist
Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, where he develops AI systems to extract intelligence from geospatial imagery bringing, cutting-edge AI solutions to the insurance and real estate industries.
UC Berkeley ML Researcher
Chuyi Shang is a machine learning researcher at Berkeley and a member of the Machine Learning @ Berkeley organization. He conducts research in video understanding and multimodal learning at Berkeley's AI Research Lab (BAIR), and has also conducted ML research at Berkeley's Haas School of Business.
UC Berkeley ML Researcher
Annabel Ng is an EECS undergrad at UC Berkeley, where she's researching brain-inspired vision models to improve image encodings in a Berkeley AI Research lab. She also leads the workshop division at Machine Learning @ Berkeley where she delivers lectures and develops interactive ML content for students.
UC Berkeley ML Researcher
Derek Xu is an ML Student Researcher at Sky Computing Lab. He's also an ML Engineer and Lecturer at ML@B and teaches a modern computer vision course at UC Berkeley. He is a 3rd year student at UCB studying EECS and Business Administration through the M.E.T. Program. In the past, he was also a SWE Intern at Salesforce.
ML Researcher at BAIR
Nathaniel Haynam is an ML Researcher at BAIR, where they push the edge of inverse reinforcement learning for multi-agent simulations. They are a ML Engineer and Lecturer in Machine Learning at Berkeley, teaching a modern computer vision course at UC Berkeley. They are a computer science major at UC Berkeley.
Technical Content Developer at Udacity
Valerie is a Sr. Technical Content Developer at Udacity who has developed and taught a broad range of computing curricula for multiple colleges and universities. She is a former professor and software engineer for over 10 years specializing in web, mobile, voice assistant, and full-stack application development.
CEO and Co-founder of LanceDB
Chang has nearly two decades of experience building and teaching data / ML tooling. He was the second major contributor to pandas, an adjunct at Columbia for introduction to data science, and ran engineering at TubiTV focusing on recommender systems. Most recently, Chang co-founded LanceDB to build the next generation database for AI.
Senior Staff Engineer
Sergei Kozyrenko is a technology leader with over 20 years of diverse industry experience - he’s built trading engines, banking software, learning management systems, co-founded an AI startup that accurately predicted street parking availability and even automated shooting of high-powered lasers at blocks of chocolate.
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Average time to complete a Nanodegree program
Generative AI
