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 and Neural Networks - Jeff Heaton
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Python Machine Learning - Sebastian Raschka
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Artificial Intelligence by example - Denis Rothman
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Machine Learning with spark and python - Michael Bowles
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Intelligent Projects Using Python - Santanu Pattanayak
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Java Deep Learning Essentials - Yusuke Sugomori
R Deep Learning Essentials - Dr. Joshua F.Wiley
The hundred-page Machine Learning Book - Andriy Burkov
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Deep Learning with Python - Francois Chollet
Deep Learning for Natural Language Processing - Jason Brownlee
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Fundamentals of Deep Learning - Nikhil Bubuma
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
An introduction to neural networks - Kevin Gurney & University of Sheffield
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar