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 from Scratch - Building with Python form First Principles - Seth Weidman
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Python Deep Learning Cookbook - Indra den Bakker
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python Data Structures and Algorithms - Benjamin Baka
Introduction to Scientific Programming with Python - Joakim Sundnes
Introduction to the Math of Neural Networks - Jeff Heaton
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Deep Learning in Python - LazyProgrammer
An introduction to neural networks - Kevin Gurney & University of Sheffield
Fundamentals of Deep Learning - Nikhil Bubuma
Pattern recognition and machine learning - Christopher M.Bishop
Machine Learning with Python for everyone - Mark E.Fenner
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Machine Learning with spark and python - Michael Bowles
Medical Image Segmentation Using Artificial Neural Networks
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Deep Learning with Hadoop - Dipayan Dev
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj