Stanford Online

Thrilled to share I’ve obtained a new certification: "XCS236 - Deep Generative Models" from Stanford Online! This was the most challenging course I've ever taken. We started with Autoregressive models (familiar territory), but quickly moved through Variational Autoencoders (VAEs), Flow Models, Generative Adversarial Networks (GANs), Energy Based Models, Score Based Models, and culminated with Diffusion Models. Went even deeper into PyTorch coding an Autoregressive GPT-2 Transformer, a few flavors of VAE (vanilla, mixture of gaussians, IWAE, and semi-supervised), a Masked Autoregressive Flow model (MAF), and a few flavors of GAN (vanilla, conditional, and Wasserstein). #genai https://lnkd.in/gPj22c2r

And Stanford's Artificial Intelligence Professional Program is finally complete. An exhaustive and deep walk through the foundational ideas and latest research in AI. I distinctly remember two moments, one with deep reinforcement learning and one with deep generative learning, in which I was lost somewhere between calculus, algebra and probability and wondering whether I really needed to do this... but both times I managed to get as deep as required. I'm not going to say the fake "I'm humbled", no... I'm proud of having done this! And I recommend it to those which are not scared of complex ideas (no simplification or shortcuts are offered in these courses), have strong mathematical foundations and don't mind investing a quite big portion of their spare time. Then it's easy. And the coding is easy once you understand the concepts well. Algebra is so powerful that makes your code fly through numpy! I’m happy to share that I’ve obtained a new certification: Artificial Intelligence from Stanford Online!

I am pleased to announce the successful completion of the Stanford Professional Certificate Program, having completed three rigorous and intellectually rewarding courses: 📘 XCS224N - 010: Natural Language Processing with Deep Learning Explored cutting-edge models and techniques in NLP, including transformers, BERT, and GPT. 📘 XCS224U - 012: Natural Language Understanding Developed a deep understanding of the principles and applications of NLU, including tasks such as sentiment analysis, entity recognition, and question answering. 📘 XCS236 - 003: Deep Generative Models Gained proficiency in advanced generative models, including VAEs, GANs, autoregressive models, normalizing flows, and energy-based models. These courses provided a solid foundation in both the theoretical and practical aspects of natural language processing, natural language understanding, and deep generative models. The program's emphasis on hands-on assignments and real-world applications has significantly enhanced my capabilities in AI and machine learning. I am excited to leverage the knowledge and skills acquired through this program to contribute to advancements in the field of artificial intelligence. Thank you to Stanford University and the instructors for providing such a transformative educational experience.

🎉 Thrilled to announce that I've earned the certification for "XCS236 - Deep Generative Models" from Stanford Online! This marks my first venture into Stanford's rigorous academic environment, and what a journey it has been! https://lnkd.in/g2pBThrT Despite not having taken the official prerequisite courses, diving into this challenging curriculum was a remarkable experience. From the foundational concepts of Autoregressive Models to the advanced intricacies of Diffusion Models, each module pushed the boundaries of my understanding. A huge thank you to Professor Stefano Ermon for his inspiring lectures and to all the course facilitators whose relentless support made this learning journey possible. The depth of knowledge covered (e.g. iterations and optimizations of Variational Autoencoders and GANs) —has been transformative. For anyone passionate about the frontier of AI and generative models, I can't recommend this course highly enough. It's a deep dive into the mechanics and potentials of generative AI that goes far beyond the basics. #AI #DeepLearning #GenerativeModels #StanfordOnline #LifelongLearning #SCPD

Great educational opportunities provided by Stanford Online dramatically helped me change my professional domain and finally "get into tech". My learning journey with Stanford Online began in 2019 with the Natural Language Processing with Deep Learning Course (XCS224N). I was so impressed by the course that I went on to complete the Artificial Intelligence Professional Program in 2020. This was followed by two graduate programs: Data Models and Optimization in 2021, and Introductory Programming in 2023. Reflecting on this journey, I believe my experiences might be useful to share with others considering a similar path. As Paul Graham eloquently stated, "Viewed as an asset, education has some attractive qualities. It naturally tends to compound. You can carry it with you easily. No one can steal or tax it. And unlike most assets, it makes life more interesting." I encourage anyone to embrace the journey of lifelong learning—it’s an investment that pays dividends in both personal and professional growth. #stanfordonline