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:

Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Medical Image Segmentation Using Artificial Neural Networks
The hundred-page Machine Learning Book - Andriy Burkov
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning with Hadoop - Dipayan Dev
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Deep Learning for Natural Language Processing - Jason Brownlee
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Data Structures and Algorithms - Benjamin Baka
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Python Machine Learning Eqution Reference - Sebastian Raschka
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Intelligent Projects Using Python - Santanu Pattanayak
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Coding Theory - Algorithms, Architectures and Application
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Java Deep Learning Essentials - Yusuke Sugomori
Deep Learning in Python - LazyProgrammer
Machine Learning with Python for everyone - Mark E.Fenner
Deep Learning with Python - Francois Cholletf
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning with Theano - Christopher Bourez
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Data Science and Big Data Analytics - EMC Education Services