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:

Neural Networks - A visual introduction for beginners - Michael Taylor
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Intelligent Projects Using Python - Santanu Pattanayak
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning and Neural Networks - Jeff Heaton
Python Data Structures and Algorithms - Benjamin Baka
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning with Theano - Christopher Bourez
Medical Image Segmentation Using Artificial Neural Networks
The hundred-page Machine Learning Book - Andriy Burkov
Deep Learning with Python - Francois Cholletf
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Pattern recognition and machine learning - Christopher M.Bishop
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
R Deep Learning Essentials - Dr. Joshua F.Wiley
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Neural Networks and Deep Learning - Charu C.Aggarwal
Deep Learning with Hadoop - Dipayan Dev
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain