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 - A Practitioner's Approach - Josh Patterson & Adam Gibson
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Introduction to the Math of Neural Networks - Jeff Heaton
Python Data Structures and Algorithms - Benjamin Baka
Python Machine Learning Eqution Reference - Sebastian Raschka
Amazon Machine Learning Developer Guild Version Latest
Deep Learning and Neural Networks - Jeff Heaton
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning with Python - Francois Cholletf
Deep Learning with Theano - Christopher Bourez
Machine Learning with Python for everyone - Mark E.Fenner
An introduction to neural networks - Kevin Gurney & University of Sheffield
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Introduction to Deep Learning - Eugene Charniak
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Python Machine Learning - Sebastian Raschka
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning for Natural Language Processing - Jason Brownlee
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Artificial Intelligence by example - Denis Rothman
Neural Networks and Deep Learning - Charu C.Aggarwal
Intelligent Projects Using Python - Santanu Pattanayak
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty