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:

The hundred-page Machine Learning Book - Andriy Burkov
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Data Science and Big Data Analytics - EMC Education Services
Artificial Intelligence by example - Denis Rothman
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Fundamentals of Deep Learning - Nikhil Bubuma
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Python Deep Learning Cookbook - Indra den Bakker
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning and Neural Networks - Jeff Heaton
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Deep Learning in Python - LazyProgrammer
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Neural Networks and Deep Learning - Charu C.Aggarwal
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Machine Learning with spark and python - Michael Bowles
An introduction to neural networks - Kevin Gurney & University of Sheffield
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Deep Learning with Theano - Christopher Bourez
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain