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:

Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Introduction to Deep Learning - Eugene Charniak
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Learn Keras for Deep Neural Networks - Jojo Moolayil
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Deep Learning with Python - Francois Chollet
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Neural Networks - A visual introduction for beginners - Michael Taylor
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Coding Theory - Algorithms, Architectures and Application
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning with Hadoop - Dipayan Dev
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Intelligent Projects Using Python - Santanu Pattanayak
Python Deep Learning Cookbook - Indra den Bakker
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Python Machine Learning - Sebastian Raschka
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Introduction to Scientific Programming with Python - Joakim Sundnes
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Machine Learning with spark and python - Michael Bowles
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Machine Learning with Python for everyone - Mark E.Fenner
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Neural Networks and Deep Learning - Charu C.Aggarwal