Grokking Deep Learning – MEAP v10 – Andrew W.Trask

It is a powerful tool for the incremental automation of intelligence.
From the beginning of time, humans have been building better and better tools to understand and control the environment around us. Deep Learning is today’s chapter in this story of innovation. Perhaps what makes this chapter so compelling is that this field is more of a mental innovation than a mechanical one. Much like its sister fields in Machine Learning, Deep Learning seeks to automate intelligence bit by bit, and in the past few years it has achieved enourmous success and progress in this endeavor, exceeding previous records in Computer Vision, Speech Recognition, Machine Translation, and many other tasks. This is particularly extraordinary given that Deep Learning seems to use largely the same brain- inspired algorithm (Neural Networks) for achieving these accomplishments across a vast number of fields. Even though Deep Learning is still an actively developing field with many challenges, recent developments have lead to tremendous excitement that perhaps we have in fact discovered more than just a great tool, but a window into our own minds as well.

Deep Learning has the potential for significant automation of skilled labor.
There is a substantial amount of hype around the potential impacts of Deep Learning if the current trend of progress is extrapolated at varying speeds. While many of these predictions are over-zealous, there is one that I think merits your consideration, job displacement. I think that this claim stands out from the rest for no other reason than if Deep Learning’s innovations stopped today, there would already be an incredible impact on skilled labor around the globe. Call center operators, taxi drivers, and low-level business analysts are compelling examples where Deep Learning can provide a low-cost alternative. Fortunately, the economy doesn’t turn on a dime, but in many ways we are already past the point of concern with the current power of the technology. It is my hope that you (and people you know) will be enabled by this book to transition from perhaps one of the industries facing disruption into an industry ripe with growth and prosperity, Deep Learning.

Related posts:

Medical Image Segmentation Using Artificial Neural Networks
Artificial Intelligence by example - Denis Rothman
Data Science and Big Data Analytics - EMC Education Services
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Deep Learning with Theano - Christopher Bourez
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Amazon Machine Learning Developer Guild Version Latest
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
The hundred-page Machine Learning Book - Andriy Burkov
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
R Deep Learning Essentials - Dr. Joshua F.Wiley
Deep Learning in Python - LazyProgrammer
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Coding Theory - Algorithms, Architectures and Application
Deep Learning and Neural Networks - Jeff Heaton
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili