About DeepLearning.AI

Chris Fregly is a Principal Solutions Architect for Generative AI at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of two O'Reilly Books: "Data Science on AWS" and "Generative AI on AWS." Chris is also the founder of the global "Generative AI on AWS" Meetup series. He regularly speaks at AI and Machine Learning conferences across the world including O’Reilly AI, Open Data Science Conference (ODSC), and Nvidia GPU Technology Conference (GTC).

Andrew Ng is the Founder of DeepLearning.AI, a General Partner at AI Fund, and the Chairman and Co-Founder of Coursera, where he also serves as an Adjunct Professor at Stanford University. Renowned for his groundbreaking contributions to machine learning and online education, Dr. Ng has transformed countless lives through his work in AI, having authored or co-authored over 100 research papers in machine learning, robotics, and related fields. His notable past roles include serving as chief scientist at Baidu and leading the founding team of Google Brain. Currently, Dr. Ng focuses on his entrepreneurial ventures, seeking innovative ways to promote responsible AI practices across the global economy.

Eda Zhou is a distinguished curriculum developer at DeepLearning.AI who combines expertise in cybersecurity with advanced machine learning applications. Having completed both her Bachelor's and Master's degrees in Computer Science from Worcester Polytechnic Institute, she specializes in developing cutting-edge AI courses, including the comprehensive Generative Adversarial Networks (GANs) Specialization. Her unique approach integrates cybersecurity principles with AI applications, focusing on user protection in computer networks. As an instructor and curriculum developer, she has created influential courses on GANs and their applications, helping students understand complex concepts from basic implementations to advanced architectures. Her work demonstrates a commitment to exploring how emerging AI technologies can enhance security measures while making these concepts accessible to learners worldwide.

Eric Zelikman is a deep learning engineer with a keen interest in how algorithms can learn meaningful representations. A recent graduate of Stanford University's Symbolic Systems program, he focuses on developing efficient, robust, and disentangled representations within the field of machine learning. Zelikman believes that insights from machine learning can inform broader human challenges, aiming to leverage this technology to address significant global issues. He is currently engaged with DeepLearning.AI and has contributed to various research initiatives that explore the intersections of language models and reasoning.

Geoff Ladwig began his career as a Deep Learning student and later became a mentor for the Deep Learning Specialization. He has served as a consultant for the Natural Language Processing Specialization and currently holds the position of Curriculum Engineer for the Machine Learning Specialization at DeepLearning.AI. With extensive experience as an ASIC, hardware, and system engineer/architect in the communications and computer industries, Geoff combines his technical expertise with a passion for education in machine learning.

Joe Reis is a prominent figure in data engineering, recognized as a best-selling author and a "recovering data scientist." He serves as a Data Engineer and Architect at DeepLearning.AI, where he also teaches courses on data engineering principles. Reis is the co-author of the acclaimed book "Fundamentals of Data Engineering" and is known for his engaging presentations as a global keynote speaker. His diverse roles include being a professor, podcaster, and writer, where he shares insights on effective data practices and the evolving landscape of data careers. Through his work, Reis aims to empower others in the field by providing practical knowledge and strategies for success in data engineering.

Laurence Moroney is an award-winning artificial intelligence researcher and best-selling author dedicated to making AI and machine learning accessible to everyone. As an instructor at DeepLearning.AI, he has taught millions through MOOCs and YouTube, while also serving as a keynote speaker at various events. Moroney is a fellow of the AI Fund and advises several AI startups, leveraging his expertise to foster innovation in the field. Based in Seattle, Washington, he is also an active member of the Science Fiction Writers of America, having authored multiple sci-fi novels and comic books. When not immersed in technology, he enjoys indulging in coffee and exploring creative writing.

Luca Martial is the Product Lead at Giskard, where he focuses on developing safety products for large language models (LLMs). His role involves iterating and launching new capabilities to ensure that LLM-based applications can be deployed with full trust from all stakeholders. Additionally, he provides guidance and support to customers during red teaming assessments of generative AI applications. Before joining Giskard, Luca led the development of the LangTest evaluation library and contributed to Spark NLP and Spark NLP for Healthcare as a data scientist, showcasing his extensive expertise in natural language processing and AI safety.

Luis Serrano is an accomplished AI scientist, popular YouTuber, and author of the book "Grokking Machine Learning." Currently, he serves as a quantum AI research scientist at Zapata Computing in Toronto, where he develops machine learning algorithms for quantum computers. Previously, he held significant roles in Silicon Valley, including lead AI educator at Apple, head of content for AI and Data Science at Udacity, and a member of the video recommendations team at Google’s YouTube. With a strong academic background, including a Bachelor's and Master's from the University of Waterloo and a PhD from the University of Michigan, Luis has a deep passion for mathematics that began in his youth when he represented Colombia in the International Mathematical Olympiads. Through his work and online presence, he aims to make complex AI concepts accessible to a broader audience while continuing to contribute to the advancement of quantum computing.

Marc Sun is a distinguished Machine Learning Engineer at Hugging Face, where he contributes to foundational open-source libraries including "transformers" and "accelerate." As an instructor for DeepLearning.AI's "Open Source Models with Hugging Face" course, he teaches developers how to leverage pre-trained models for various AI tasks including NLP, audio, image, and multimodal applications. His work on the "accelerate" library has helped simplify PyTorch model training across different hardware configurations, while his contributions to the "transformers" library enable developers to easily access and implement state-of-the-art pretrained models. Through his teaching and development work, Sun helps make advanced AI technologies more accessible to developers worldwide, focusing on practical applications and efficient implementation strategies.

Matteo Dora is a distinguished machine learning researcher leading the LLM safety team at Giskard, where he specializes in developing and implementing security measures for AI systems. As an instructor for DeepLearning.AI's "Red Teaming LLM Applications" course, he teaches developers how to identify and mitigate vulnerabilities in large language models. His expertise spans the intersection of ethics, safety, and security in AI applications, with particular focus on conducting red team assessments of generative AI systems. Prior to his current role, Dora worked as an academic researcher in neuroscience and applied mathematics, bringing a unique interdisciplinary perspective to AI safety. His work at Giskard has contributed to the development of open-source testing frameworks for machine learning models, helping organizations identify critical vulnerabilities such as prompt injection, sensitive information disclosure, and hallucination

Mike Chambers is a distinguished Developer Advocate for Generative AI at Amazon Web Services (AWS) with over 20 years of experience in the ICT industry. As an AWS Machine Learning Hero and celebrated trainer, he has educated more than 250,000 students worldwide in cloud computing, serverless architecture, and machine learning. His career spans roles as a Solutions and Security Architect across large multinationals, government organizations, and his own successful startup. Through his work at DeepLearning.AI, he develops courses on generative AI and large language models, making complex technologies accessible to learners globally. His contributions to the AWS community earned him recognition as an AWS Machine Learning Hero in 2020, acknowledging his commitment to knowledge sharing through social media, AWS Meetups, and study groups worldwide.

Pranav Rajpurkar is a distinguished AI researcher and Assistant Professor at Harvard University's Department of Biomedical Informatics, where he leads groundbreaking research in medical AI applications. With degrees from Stanford University, including a Ph.D. co-advised by Andrew Ng and Percy Liang, he has made significant contributions to developing expert-level deep learning algorithms for medical imaging across specialties including radiology, cardiology, and pathology. As an educator, he created and instructed the popular Coursera AI for Medicine Specialization, which has enrolled over 24,000 students. His work extends beyond academia through co-hosting The AI Health Podcast and co-editing the Doctor Penguin AI Health Newsletter. Through his research lab at Harvard, he focuses on developing AI algorithms, datasets, and interfaces that bridge computer vision, natural language processing, and structured health data, while exploring effective clinician-AI collaboration models

Robert Monarch (Munro) is a distinguished AI expert and author who specializes in integrating human and machine intelligence for practical applications. With a Ph.D. from Stanford University and over 20 years of experience, he has contributed to AI development at major tech companies including Google, Amazon, Microsoft, and Apple. As the author of "Human-in-the-Loop Machine Learning," he provides foundational insights into combining human and machine intelligence effectively. His work spans critical applications in disaster response and public health worldwide, demonstrating AI's potential for social impact. Through his role at DeepLearning.AI, he develops courses that make complex AI concepts accessible to learners globally, while his entrepreneurial ventures in AI startups showcase his commitment to practical innovation. His approach emphasizes the importance of human oversight in machine learning systems, ensuring AI solutions remain both effective and ethically sound

Sharon Zhou is a distinguished AI researcher and entrepreneur who uniquely combines classical education with cutting-edge technology. As co-founder and CEO of Lamini, she develops innovative LLM solutions while maintaining her role as an influential educator through DeepLearning.AI. A Stanford Ph.D. graduate advised by Andrew Ng, she created one of Coursera's largest classes on Generative Adversarial Networks, reaching over 250,000 students. Zhou holds the distinction of being the first to major in both Classics and Computer Science at Harvard University, graduating summa cum laude in both fields. Named in MIT Technology Review's 35 Under 35, she serves as an AI advisor to Washington D.C. policymakers while advancing her mission of making AI technology accessible to everyone. Her research spans theoretical and applied AI in medicine, climate, and social good, building on her experience as a former machine learning product manager at Google

Shelbee Eigenbrode is a distinguished Principal Solutions Architect for Generative AI at Amazon Web Services (AWS), bringing 23 years of diverse technology experience to her role. With over 30 patents across various technology domains and 6 AWS certifications, she specializes in combining DevOps and machine learning expertise to manage MLOps/FMOps/LLMOps workloads at scale. As co-creator and instructor of both the 'Practice Data Science on the AWS Cloud Specialization' and 'Generative AI with Large Language Models' courses on Coursera, she helps make complex AI concepts accessible to learners worldwide. Her commitment to diversity in tech is demonstrated through her role as Co-Director of the Denver chapter of Women in Big Data, where she actively works to expand opportunities for women in the field. Through her published works and continuous innovation, she maintains her focus on using data to drive business outcomes while fostering an inclusive tech community.

Tim Tully is a distinguished technology leader and venture capitalist who currently serves as a Partner at Menlo Ventures, focusing on early-stage investments in AI/ML, cloud infrastructure, and cybersecurity. With an impressive career spanning leadership roles at major tech companies, he previously served as CTO of Splunk, where he led the company's cloud transition and scaled the product team from 600 to 1,800 people, helping double revenue to $2.25 billion. Before Splunk, he spent 14 years at Yahoo!, rising from data engineer to Chief Data Architect, where he contributed to the development of Hadoop. As a board member at DeepLearning.AI and developer of Pinecone's VectorDB Command Line Interface, he continues to advance AI education and technology innovation. Tully holds degrees from Carnegie Mellon University (M.S.) and the University of California, Davis (B.S.), and actively participates in hackathons while teaching vector database courses with Andrew Ng

Younes Belkada is a distinguished Machine Learning Engineer at Hugging Face, where he plays a crucial role in democratizing AI through open-source development. As a key contributor to essential libraries including transformers, PEFT (Parameter Efficient Fine-Tuning), and TRL (Transformers Reinforcement Learning), he has authored numerous influential articles on AI advancement, including comprehensive guides on model quantization, mixture of experts, and large language model fine-tuning. Through his role at DeepLearning.AI, he teaches the popular "Open Source Models with Hugging Face" course, helping developers leverage pre-trained models for various AI tasks. His work focuses on making advanced AI technologies more accessible, particularly through innovations in model quantization and efficient fine-tuning techniques. Belkada's contributions have significantly impacted the AI community, as evidenced by his extensive portfolio of technical articles and his role in developing crucial tools for AI democratization.

Younes Bensouda Mourri is a distinguished AI educator and entrepreneur who has significantly impacted global tech education. Born and raised in Morocco, he earned his B.S. in Applied Mathematics and Computer Science and M.S. in Statistics from Stanford University, where he now teaches Artificial Intelligence both on campus and online. As the founder of LiveTech.AI, he develops AI tools to transform academic institutions, while his courses have reached over 1.3 million learners worldwide, with 23% securing AI-related jobs after completion. His contributions include co-creating Stanford's Applied Machine Learning, Deep Learning, and Teaching AI courses, as well as developing the highly successful Natural Language Processing Specialization for DeepLearning.AI. Starting as a teaching assistant in Andrew Ng's Machine Learning course, he rose to become an Adjunct Lecturer at Stanford by age 22, demonstrating his commitment to democratizing AI education. Through his work with major companies like ASML, CISCO, and Boston Consulting Group, he continues to advance AI education while focusing on developing innovative NLP tools for personalized feedback and chain-of-thought reasoning

Antje Barth is a distinguished Principal Developer Advocate for Generative AI at Amazon Web Services (AWS), where she leads initiatives in artificial intelligence education and implementation. As co-author of two influential O'Reilly books, "Data Science on AWS" and "Generative AI on AWS," she has established herself as a leading voice in cloud-based AI solutions. Through her role at DeepLearning.AI, she co-developed the comprehensive course "Generative AI with Large Language Models," which provides practical insights into building and deploying AI applications. Her expertise spans from technical evangelism to solutions engineering, with previous roles at Cisco and MapR focusing on data center technologies and big data applications. As co-founder of the Düsseldorf chapter of Women in Big Data, Barth actively promotes diversity in technology while regularly speaking at global AI/ML conferences and events to share her knowledge and insights in generative AI development.