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:

Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Deep Learning in Python - LazyProgrammer
Deep Learning for Natural Language Processing - Jason Brownlee
Introduction to Scientific Programming with Python - Joakim Sundnes
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Machine Learning with Python for everyone - Mark E.Fenner
An introduction to neural networks - Kevin Gurney & University of Sheffield
Medical Image Segmentation Using Artificial Neural Networks
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Machine Learning Eqution Reference - Sebastian Raschka
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Neural Networks and Deep Learning - Charu C.Aggarwal
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Introduction to the Math of Neural Networks - Jeff Heaton
Python Data Structures and Algorithms - Benjamin Baka
Deep Learning with Theano - Christopher Bourez
Machine Learning with spark and python - Michael Bowles
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Python Machine Learning - Sebastian Raschka
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Fundamentals of Deep Learning - Nikhil Bubuma
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...