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:

Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning for Natural Language Processing - Jason Brownlee
Python Machine Learning Eqution Reference - Sebastian Raschka
Neural Networks - A visual introduction for beginners - Michael Taylor
Neural Networks and Deep Learning - Charu C.Aggarwal
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Machine Learning with Python for everyone - Mark E.Fenner
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Data Structures and Algorithms - Benjamin Baka
Medical Image Segmentation Using Artificial Neural Networks
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning with Python - Francois Chollet
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Deep Learning with PyTorch - Vishnu Subramanian
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Fundamentals of Deep Learning - Nikhil Bubuma
Python Deep Learning Cookbook - Indra den Bakker
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Intelligent Projects Using Python - Santanu Pattanayak