Introducing: The Deep Learning AI Playbook

Exploit Deep Learning: The Deep Learning AI Playbook

Permit me the luxury of introducing a book that I’ve been working on for the past year. For readers of this blog, I’m sure you’ve seen this book mentioned in a many of my posts. However, I have yet to properly introduce readers about the book. This is finally my opportunity to do so.

The book has its own website, which should be very easy to remember: “Deep Learning”. Every new content in today’s world has to compete for the eyeballs and time of its customers. That’s where selling a book is a very difficult proposition, no matter how valuable the content may be. Who has the time to slog through 350 pages of text? Unfortunately, for complex and emerging subjects like Deep Learning, there aren’t any instant ways to download knowledge and wisdom into one’s own brains. If there were a way to make knowledge transfer even easier then I would gladly like to know what that magic potion will be.

Writing a book takes considerable mental effort. This does not include all the other administrivia that is required for a book (i.e. cover design, layout, proof reading, copy editing etc.). The most difficult part of writing is a book is the generation of ideas. Fortunately, I have this blog and its many readers who have given me feedback, ideas and criticism that has helped me to improve and fine tune new ideas.

A book is always never really finished. You will always find new and more compelling concepts and ways to present content. So as it is best practice in agile environments, its always best to confine scope by time-boxing the process. Also, it’s always best than to be late. Knowledge discovery will always be an iterative process and book writing is essentially a knowledge discovery process.

Forcing oneself to write is like photography. When you are into photography, ,that is when you’ve decided consciously to capture the best angles of an event or place that you find yourself in, you are consciously aware of the environment at a higher level than a casual visitor. You are aware of the lighting, shadows, interaction of people, shooting angles etc. With writing about a subject it is similar. You put yourself in a perspective that many may not be aware of. That is why, photographers can learn from other photographers and writers can learn from other writers. I am grateful for the perspectives that other writers have lent to me.

A book is always best sold with testimonials. I am fortunate to have found people who have taken the time to go through my book and lend their impressions. Here are a few of the notable ones:

I love it. Chapters 3, 4, 8 and 9. and your approach thru the lens of intuition!

— John Seely Brown, Author and cofounder of the Institute for Research on Learning

Then it continues to the principles and best practices, followed by cutting-edge research. Many of the ideas actually lead towards what we call AGI (general AI) The book takes a deep dive into “meta learning (learning to learn)”, which I believe is the most efficient way to automate engineering of a thinking machine: bootstrapping itself and recursively self-improving its adaptability.”

– Marek Rosa, CTO GoodAI

His playbook has given me great inspiration on the latest topics and viewpoints for my deep learning lectures. Our students and alumni consider this a helpful reference and strategy guide as they find new uses for deep learning and AI in industry.”

– Ellick Chan
Adjunct Lecturer MSiA 432: Deep Learning
Northwestern University

His is a refreshingly different approach to AI It is easy to read and at the same time covers a lot of complexity and detail.

Ajit Jaokar, Director of AI / Deep Learning Lab for Future Cities, University of Madrid

Finally, a word about the economics of publishing a book. Most people who have never written a book are unaware of the brutal economics. I have chosen to self-publish. Gumroad is a god-send for writers in that a least 95% of the purchase price goes to the original author. However, when a writer goes with a publisher, he would find himself lucky to even receive 12% of the net revenue. A printed book has its own cost, a standard 6"x9" paperback costs around $5 to produce. A hardbound version, costs a lot more (i.e. $12). After that, the distributors are also going to get their cut. So depending on which distributor you use, they can get at least a 40%-65% cut from the sales price. So, just to put this in perspective, a writer who sells a book via a publisher for $50 should expect to receive a paltry $3.24 ( (50–5)*.6*.12) in revenue. Try feeding your family on those wages! Very little goes to the author will all these middle men taking their cut. So don’t go into writing a book for the money, do so because you are passionate about the subject and if you want to be fairly compensated, get rid of the middle men.

That’s what I did.

Introducing: The Deep Learning AI Playbook was originally published in Intuition Machine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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