Author: Wendell C. Stone
Publisher: SIU Press
“It’s Magic Time!” That colorful promise began each performance at the Caffe Cino, the storied Greenwich Village coffeehouse that fostered the gay and alternative theatre movements of the 1960s and launched the careers of such stage mainstays as Sam Shepard, Lanford Wilson, Robert Heide, Harry Koutoukas, Robert Patrick, Robert Dahdah, Helen Hanft, Al Pacino, and Bernadette Peters. As Off-Off-Broadway productions enjoy a deserved resurgence, theatre historian and actor Wendell C. Stone reopens the Cino’s doors in this vibrant look at the earliest days of OOB. Rife with insider interviews and rich with evocative photographs, Caffe Cino: The Birthplace of Off-Off-Broadway provides the first detailed account of Joe Cino’s iconic café theatre and its influence on American theatre. A hub of artistic innovation and haven for bohemians, beats, hippies, and gays, the café gave a much-sought outlet to voices otherwise shunned by mainstream entertainment. The Cino’s square stage measured only eight feet, but the dynamic ideas that emerged there spawned the numerous alternative theatre spaces that owe their origins to the risky enterprise on Cornelia Street.
Author: Jocelyn Arem
Publisher: powerHouse Books
In 1960, burgeoning actress and defiant dreamer Lena Spencer opened a small, grassroots coffeehouse in the quaint upstate New York town of Saratoga Springs. Within her then-husband’s plan to start the Caffè as a means for the couple to artistically flourish while “making enough money to retire in Europe” lay the seed of a more impactful cultural contribution that would change music history forever. It was a time in America when a coffeehouse could be something more—a focal point for a different sort of people, radical new ideas, and notably, emerging artists. Caffè Lena’s humble stage regularly welcomed musicians such as a young Bob Dylan in 1961, the singer/activist Bernice Johnson Reagon in 1962, and a pre-”American Pie” Don McLean in 1965. Quickly, Caffè Lena took its place among the nation’s foremost incubators of an American folk movement that inspired a generation of musicians, artists, and thinkers and a country in need of a new vision of equality, freedom, and understanding. Fortunately for posterity, camera shutters were often snapping in time to the music, and so an intimate visual record of Caffè Lena’s early years exists. Now, thanks to years of dedicated digging by the Caffè Lena History Project—to unearth Lena’s secret memoirs, collaborating with photographers to identify and rescue mysterious negatives, and collecting stories from the original artists to highlight these materials—the time has come to share this treasure trove of authentic and rare Americana with the world. Caffè Lena: Inside America’s Legendary Folk Music Coffeehouse brings more than 200 never before seen, evocative images and stories to the public. Early 1960s photographs of Bob Dylan and Pete Seeger and modern-day images of Rufus Wainwright and Patty Larkin blend with rare memorabilia and an oral history derived from more than 100 original interviews of artists who have graced Caffè Lena’s stage over the decades, including Ani DiFranco, Utah Phillips, Dave Van Ronk, Spalding Gray, and other luminaries of the folk, blues, jazz, and theater worlds. This exclusive time capsule chronicling the heyday of Caffè Lena—now the country’s oldest continuously operational folk music coffeehouse—provides an insightful look at the many artists whose poetic lyrics cast a mesmerizing spell over a generation, and who remain beloved today. Alongside the release of Caffè Lena: Inside America’s Legendary Folk Music Coffeehouse, San Francisco’s Tompkins Square label will release the 3-CD box set, ‘Live at Caffè Lena: Music From America’s Legendary Coffeehouse, 1967–2013′ on September 24, 2013. “Caffè Lena holds an important place in the folk and traditional music communities. For me it was the gateway to so many things I hold dear about music.” —Scott Goldman, The GRAMMY Foundation “The story of Caffè Lena is the secret history of the folk-music scene. Lena was a pioneering woman in a man's world and her story needs to be told.” —Holly George-Warren, The Road to Woodstock “Lena Spencer was a rare person with a shining spirit who created a small world of her own. The magic of her Caffè cannot be analyzed, computerized, or explained.” —David Amram, Musician
Caldo, denso, di colore scuro: è il caffè. Chiamarlo bevanda è riduttivo: il caffè è un rito, una pausa di piacere, qualcosa che fa parte dell’identità partenopea più profonda. Non esiste altro luogo al mondo dove il rito del caffè abbia lo stesso sapore che ha a Napoli. Questo libro racconta la storia del nero infuso, che arriva da lontano, e che all’ombra del Vesuvio ha trovato la sua patria. E accompagna il lettore in un viaggio affascinante tra gli antichi Caffè letterari e quelli di oggi, che con le loro attività culturali sono i germogli di una nuova Primavera per la città.
Return to the Caffe Cino
Author: Steve Susoyev
Publisher: Moving Finger Press
RETURN TO THE CAFFE CINO gives a fresh, exciting portrait of the non-commercial NY theater scene in the 1960's. The scene is painted here by dozens of short essays by the artists that were a part of the creative fission that flared so brightly there and that still influences so much of today's theatre. The eyewitness stories are usually hysterically funny, filled with that sense of freedom that ignited a movement that continues today in small independent theaters. And the editors of the anthology have filled the pages with vintage pictures, including one of a fifteen-year-old Bernadette Peters getting her start at the Caffe Cino!
“Volete sostenere una società libertaria bevendo caffè? “Signore, Signora, vi piacerebbe bere un buon caffè per sostenere la lotta del popolo Maya messicano che lo ha prodotto e per aiutarlo ad uscire dalla povertà? Loro non vogliono essere costretti a emigrare illegalmente negli Stati Uniti, e non vogliono avere padroni, ma producono un caffè molto buono, è il migliore caffè biologico del mondo! E a voi costa meno del caffè che acquistate al supermercato! Volete provarlo?” Il North American Free Trade Agreement (NAFTA), firmato il 1 gennaio 1994, è stato presentato come l’ascesa del Messico in un moderno stato del primo mondo. Ma, nello stato meridionale del Chiapas in quel Capodanno, una “rivolta armata dei popoli indigeni ha rubato i riflettori dei media, esponendo le enormi disuguaglianze sociali del Messico e l’esclusione della popolazione indigena del paese dal suo sviluppo economico”, (latino-american Press, gennaio 20,1994).
Learn from the professionals - a must-have guide to making the best coffee. Many people like to kick-start their day with a really good cup of coffee. This is the book for them! It gives a brief outline of how and where coffee is grown and processed and then 'spills the beans' on how to get the best cup your chosen equipment can provide - whether you use a plunger, filter, stovetop or full-blown espresso machine. Caffe L'affare first produced this book for the baristas who take a short training course with them. Then the book was developed for the general market and the home user. Now it has been expanded to add further info on coffee tasting, production sustainability and the roasting process, along with hot tips, new step-by-step preparations, an all-encompassing glossary and a bright new look. Learn from the professionals and enhance your enjoyment of your coffee.
Offers two hundred recipes for fresh pizzas, focaccia, tarts, breads, snacks, and pastas
- La rinascita di un mito- La bevanda sovversiva- Polvere di salute e di bellezza- L'arte di degustare il caffè- Menu all'aroma di caffè
Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java About This Book Go beyond the theory and put Deep Learning into practice with Java Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe Whether you're a data scientist or Java developer, dive in and find out how to tackle Deep Learning Who This Book Is For This book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment. What You Will Learn Get a practical deep dive into machine learning and deep learning algorithms Implement machine learning algorithms related to deep learning Explore neural networks using some of the most popular Deep Learning frameworks Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Gain an insight into the deep learning library DL4J and its practical uses Get to know device strategies to use deep learning algorithms and libraries in the real world Explore deep learning further with Theano and Caffe In Detail AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you'll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today. By the end of the book, you'll be ready to tackle Deep Learning with Java. Wherever you've come from – whether you're a data scientist or Java developer – you will become a part of the Deep Learning revolution! Style and approach This is a step-by-step, practical tutorial that discusses key concepts. This book offers a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. It is packed with implementations from scratch, with detailed explanation that make the concepts easy to understand and follow.
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.