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:

Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Introduction to the Math of Neural Networks - Jeff Heaton
Introduction to Deep Learning - Eugene Charniak
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
The hundred-page Machine Learning Book - Andriy Burkov
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Neural Networks and Deep Learning - Charu C.Aggarwal
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning with Python - Francois Chollet
Neural Networks - A visual introduction for beginners - Michael Taylor
Data Science and Big Data Analytics - EMC Education Services
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning with Python - Francois Cholletf
Python Data Structures and Algorithms - Benjamin Baka
Pattern recognition and machine learning - Christopher M.Bishop
Java Deep Learning Essentials - Yusuke Sugomori
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning for Natural Language Processing - Jason Brownlee
R Deep Learning Essentials - Dr. Joshua F.Wiley