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:

Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Coding Theory - Algorithms, Architectures and Application
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Medical Image Segmentation Using Artificial Neural Networks
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning with Applications Using Python - Navin Kumar Manaswi
An introduction to neural networks - Kevin Gurney & University of Sheffield
Introduction to Scientific Programming with Python - Joakim Sundnes
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning with PyTorch - Vishnu Subramanian
Amazon Machine Learning Developer Guild Version Latest
Deep Learning with Python - Francois Chollet
R Deep Learning Essentials - Dr. Joshua F.Wiley
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Intelligent Projects Using Python - Santanu Pattanayak
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Python Machine Learning - Sebastian Raschka
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Deep Learning with Hadoop - Dipayan Dev
Neural Networks and Deep Learning - Charu C.Aggarwal
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Deep Learning Cookbook - Indra den Bakker
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido