James S. Duncan is Lecturer at the University of Cambridge and a Fellow of Emmanuel College, Cambridge.
Nuala C. Johnson is Reader at Queen's University, Belfast.
Richard H. Schein is Associate Professor at the University of Kentucky.
There is a home out there for every song you've written, but in order to place those songs and advance your music career you must arm yourself with steadfast determination, unending passion, and the most accurate music business knowledge available. For more than 38 years, Songwriter's Market has provided songwriters and performing artists with the most complete and up-to-date information needed to place songs with music publishers, find record companies and producers, obtain representation with managers, and more. This comprehensive guide gives you the tools and first-hand knowledge you need to launch your songwriting career right now!
In the 2015 edition, you'll also gain access to: A new foreword by hit songwriter and best-selling author Jason Blume New interviews with music publishers, Grammy Award-winning producers, and major music industry leaders Articles about how to create and mix a professional demo at home, how to get the most out of music conferences, and much more Hundreds of songwriting placement opportunities Listings for songwriting organizations, conferences, workshops, retreats, colonies, contests, venues, and grant sources (helpful for indie artists looking to record and tour on their own) *Includes access to the webinar "Song Seeds: How to Jump-start Your Songwriting Process" from author and Berklee College of Music professor Mark Simos.
The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details