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:

Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Machine Learning with spark and python - Michael Bowles
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Python Data Structures and Algorithms - Benjamin Baka
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Artificial Intelligence by example - Denis Rothman
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Machine Learning - Sebastian Raschka
Amazon Machine Learning Developer Guild Version Latest
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning in Python - LazyProgrammer
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Deep Learning with Python - Francois Cholletf
Deep Learning and Neural Networks - Jeff Heaton
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Introduction to Deep Learning - Eugene Charniak
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...