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:

Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Pattern recognition and machine learning - Christopher M.Bishop
Java Deep Learning Essentials - Yusuke Sugomori
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Introduction to Deep Learning - Eugene Charniak
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Deep Learning with PyTorch - Vishnu Subramanian
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Deep Learning with Python - Francois Chollet
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Neural Networks and Deep Learning - Charu C.Aggarwal
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Amazon Machine Learning Developer Guild Version Latest
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
An introduction to neural networks - Kevin Gurney & University of Sheffield
Deep Learning in Python - LazyProgrammer
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Coding Theory - Algorithms, Architectures and Application