Generative Deep Learning – Teaching Machines to Paint, Write, Compose and Play – David Foster

An undeniable part of the human condition is our ability to create. Since our earliest days as cave people, we have sought opportunities to generate original and beautiful creations. For early man, this took the form of cave paintings depicting wild animals and abstract patterns, created with pigments placed carefully and methodically onto rock. The Romantic Era gave us the mastery of Tchaikovsky symphonies, with their ability to inspire feelings of triumph and tragedy through sound waves, woven together to form beautiful melodies and harmonies. And in recent times, we have
found ourselves rushing to bookshops at midnight to buy stories about a fictional wizard, because the combination of letters creates a narrative that wills us to turn the page and find out what happens to our hero.

It is therefore not surprising that humanity has started to ask the ultimate question of creativity: can we create something that is in itself creative?
This is the question that generative modeling aims to answer. With recent advances in methodology and technology, we are now able to build machines that can paint origi‐nal artwork in a given style, write coherent paragraphs with long-term structure, compose music that is pleasant to listen to, and develop winning strategies for com‐plex games by generating imaginary future scenarios. This is just the start of a gener‐ative revolution that will leave us with no choice but to find answers to some of the biggest questions about the mechanics of creativity, and ultimately, what it means to be human. In short, there has never been a better time to learn about generative modeling—so let’s get started!

Related posts:

Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Data Science and Big Data Analytics - EMC Education Services
Introduction to the Math of Neural Networks - Jeff Heaton
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Deep Learning in Python - LazyProgrammer
Deep Learning with Python - Francois Cholletf
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
An introduction to neural networks - Kevin Gurney & University of Sheffield
Deep Learning and Neural Networks - Jeff Heaton
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Machine Learning Eqution Reference - Sebastian Raschka
Artificial Intelligence by example - Denis Rothman
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Intelligent Projects Using Python - Santanu Pattanayak
Python Machine Learning - Sebastian Raschka
Fundamentals of Deep Learning - Nikhil Bubuma
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Neural Networks - A visual introduction for beginners - Michael Taylor
Introduction to Deep Learning - Eugene Charniak
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Coding Theory - Algorithms, Architectures and Application
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Machine Learning with spark and python - Michael Bowles
Neural Networks and Deep Learning - Charu C.Aggarwal