front matter
foreword
I first met Mark at the PNC Innovation Summit at the University of Kentucky, at which we were both presenters. His topic was How Machines Learn. From our very first encounter, I was struck by Mark’s ability to explain complex concepts in an engaging and easy-to-understand manner. His knack for breaking down intricate ideas into digestible, relatable terms was truly impressive, and it’s a gift that he now shares through his latest book, Learn Generative AI with PyTorch.
At Native AI, where I am cofounder and chief operating officer, we are tasked with generating predictive synthetic data that is both highly accurate and robust. Mark’s exploration of techniques like temperature and top-K sampling to control the precision of AI-generated text is cutting-edge. These methods are essential for tailoring natural language processing outputs to specific use cases, a topic that will continue to grow in importance and business value.
Learn Generative AI with PyTorch is a comprehensive guide that not only introduces readers to the fascinating world of generative AI but also equips them with practical skills to build and implement their own models. Mark’s use of PyTorch as the framework of choice is a testament to its flexibility and power in developing advanced AI models. From long short-term memory models to variational autoencoders, generative adversarial networks, and Transformers, this book covers an impressive breadth of topics.