Hey,

I want to share a personal story with you:

Toward the end of my graduate school career (2013-2014), I wanted to start wrapping my head around this whole "deep learning" thing.

I was in a very unique situation. My dissertation was (essentially) wrapped up. Each of my Ph.D committee members had signed off on it. However, due to university/department regulations, I still had an extra semester that I needed to "hang around" for before I could officially defend my dissertation and graduate.

This essentially left me with an entire semester (4 months) to kill — it was an excellent time to start studying deep learning.

My first stop, as is true for most academics, was to read through all the recent publications on deep learning. Due to my machine learning background, it didn't take long to grasp the actual theoretical foundations of deep learning.

However, I'm of the opinion that until you actually take your theoretical knowledge and implement it, you haven't actually learned anything yet. Transforming theory to implementation is a very different process.

And that's exactly what the problem was for me.

After reading these deep learning publications, I was left scratching my head, trying (unsuccessfully) to take what I learned from the papers and implement the actual algorithms, let alone reproduce the results.

Frustrated with my failed attempts at implementation, I spent hours searching on Google, hunting for deep learning tutorials, only to come up empty-handed. Back then, there weren't many deep learning tutorials to be found.

Finally, I resorted to playing around with libraries and tools such as Caffe, Theano, and Torch, blindly following poorly written blog posts (with mixed results, to say the least).

I wanted to get started, but nothing had actually clicked yet — the deep learning lightbulb in my head was stuck in the "off" position.

To be totally honest with you, it was a painful, emotionally trying semester. I could clearly see the value of deep learning for computer vision, but I had nothing to show for my effort, except for a stack of deep learning papers on my desk that I understood but struggled to implement.

During the last month of the semester, I finally found my way to deep learning success through hundreds of trial-and-error experiments, countless late nights, and a lot of perseverance.

In the long run, those four months made a massive impact on my life, my research path, and how I understand and work with deep learning today...

...but I would not advise you to take the same path I did.

If you take anything away from my personal experience, it should be this:
  1. You don't need a decade of theory to get started in deep learning.
  2. You don't need pages and pages of equations.
  3. And you certainly don't need a degree in computer science (although it can be helpful).
To get started in deep learning, all you need is this:

A book that not only thoughtfully and meticulously presents deep learning + visual recognition algorithms, but also provides implementations of them.

Sounds perfect, right?

If someone would just explain the actual algorithms in code rather than in pages of equations, it would totally "click" for you — the lightbulb would flip "on", and you would finally see the path to deep learning enlightenment.

If that sounds like you, I've got good news:

That's exactly what you'll see in my new book, Deep Learning for Computer Vision with Python.

Inside this book, you'll find:
  • Super practical walkthroughs that present solutions to actual, real-world deep learning classification problems, challenges, and competitions.
  • Hands-on tutorials (with lots of code) that not only show you the algorithms behind deep learning for computer vision, but their implementations as well.
  • A no-nonsense teaching style that is guaranteed to cut through all the cruft and help you master deep learning for computer vision and visual recognition.
So, what do you say?

Do you want to join me in deep learning mastery?

If so, you can grab your copy of my new book using this link:

>> Grab your copy now

Otherwise, I'll be back in your inbox tomorrow discussing a question I received from PyImageSearch reader Alexei on which Deep Learning for Computer Vision with Python bundle to choose.

See you then!

Adrian Rosebrock
Chief PyImageSearcher

P.S. Have any questions regarding Deep Learning for Computer Vision with Python? If so, reply to this email and let me know. I'll get back to you ASAP.