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:

Medical Image Segmentation Using Artificial Neural Networks
Deep Learning for Natural Language Processing - Jason Brownlee
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Data Science and Big Data Analytics - EMC Education Services
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Deep Learning in Python - LazyProgrammer
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Java Deep Learning Essentials - Yusuke Sugomori
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning with Theano - Christopher Bourez
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Python Data Structures and Algorithms - Benjamin Baka
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning and Neural Networks - Jeff Heaton