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:

Data Science and Big Data Analytics - EMC Education Services
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Python Machine Learning Eqution Reference - Sebastian Raschka
Neural Networks - A visual introduction for beginners - Michael Taylor
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Fundamentals of Deep Learning - Nikhil Bubuma
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Intelligent Projects Using Python - Santanu Pattanayak
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Neural Networks and Deep Learning - Charu C.Aggarwal
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Amazon Machine Learning Developer Guild Version Latest
Deep Learning in Python - LazyProgrammer
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Introduction to Deep Learning - Eugene Charniak
Artificial Intelligence by example - Denis Rothman
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Medical Image Segmentation Using Artificial Neural Networks
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Introduction to Scientific Programming with Python - Joakim Sundnes
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf