Together to the Tune of Coltrane's Equinox" is a collection of Sarah Webster Fabio's poems that pay tribute to all musicians/magicians named and unnamed, known and unknown who through love- translated their lives into improvisation, harmony, jazz, blues, rock and roll. It was dedicated to all of those "down home soul beats" which remind Black folks that they are a folk of SOUL.Sarah Webster Fabio (1/20/1928-11/7/1979) was a poet, creative writer, and scholar. She is said to be the mother of Black Studies.
In Revolutionary Poetics, Sarah RudeWalker details the specific ways that the Black Arts Movement (BAM) achieved its revolutionary goals through rhetorical poetics—in what forms, to what audiences, and to what effect. BAM has had far-reaching influence, particularly in developments in positive conceptions of Blackness, in the valorization of Black language practices and its subsequent effects on educational policy, in establishing a legacy of populist dissemination of African American vernacular culture, and in setting the groundwork for important considerations of the aesthetic intersections of race with gender and sexuality. These legacies stand as the movement’s primary—and largely unacknowledged—successes, and they provide significant lessons for navigating our current political moment. RudeWalker presents rhetorical readings of the work of BAM poets (including, among others, Amiri Baraka, Gwendolyn Brooks, Margaret Burroughs, Sarah Webster Fabio, Nikki Giovanni, Etheridge Knight, Audre Lorde, Haki Madhubuti, Carolyn Rodgers, Sonia Sanchez, and the Last Poets) in order to demonstrate the various strands of rhetorical influence that contributed to the Black Arts project and the significant legacies these writers left behind. Her investigation of the rhetorical impact of Black Arts poetry allows her to deal realistically with the movement’s problematic aspects, while still devoting thoughtful scholarly attention to the successful legacy of BAM writers and the ways their work can continue to shape contemporary rhetorical activism.
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.
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