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:

Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Amazon Machine Learning Developer Guild Version Latest
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Artificial Intelligence by example - Denis Rothman
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
The hundred-page Machine Learning Book - Andriy Burkov
Medical Image Segmentation Using Artificial Neural Networks
Machine Learning with Python for everyone - Mark E.Fenner
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Intelligent Projects Using Python - Santanu Pattanayak
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Deep Learning with Python - Francois Chollet
Pattern recognition and machine learning - Christopher M.Bishop
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Python Machine Learning Eqution Reference - Sebastian Raschka
Python Machine Learning - Sebastian Raschka
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Neural Networks and Deep Learning - Charu C.Aggarwal
An introduction to neural networks - Kevin Gurney & University of Sheffield