But sneaking into museums, purchasing three-cent copies of National Geographic, and devouring books on the history of science ignited in Dresselhaus (1930-2017) a passion for inquiry. In Carbon Queen, science writer Maia Weinstock describes how, with curiosity and drive, Dresselhaus defied expectations and forged a career as a pioneering scientist and engineer. Dresselhaus made highly influential discoveries about the properties of carbon and other materials and helped reshape our world in countless ways--from electronics to aviation to medicine to energy. She was also a trailblazer for women in STEM and a beloved educator, mentor, and colleague.Her path wasn't easy. Dresselhaus's Bronx childhood was impoverished. Her graduate adviser felt educating women was a waste of time. But Dresselhaus persisted, finding mentors in Nobel Prize-winning physicists Rosalyn Yalow and Enrico Fermi. Eventually, Dresselhaus became one of the first female professors at MIT, where she would spend nearly six decades. Weinstock explores the basics of Dresselhaus's work in carbon nanoscience accessibly and engagingly, describing how she identified key properties of carbon forms, including graphite, buckyballs, nanotubes, and graphene, leading to applications that range from lighter, stronger aircraft to more energy-efficient and flexible electronics.

    If you're a data scientist or machine learning engineer, this practical book shows you how to train and scale these large models using HuggingFace Transformers, a Python-based deep learning library.Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf use a hands-on approach to teach you how Transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve.* Build, debug, and optimize Transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering* Learn how Transformers can be used for cross-lingual transfer learning* Apply Transformers in real-world scenarios where labeled data is scarce* Make Transformer models efficient for deployment using techniques such as distillation, pruning, and quantization* Train Transformers from scratch and learn how to scale to multiple GPUs and distributed environments