A Deep Belief Network(DBN) is a powerful generative model that uses a deep architecture and in this article we are going to learn all about it. Don’t worry this is not relate to ‘The Secret or Church’, even though it involves ‘Deep Belief’, I promise!
After you read this article you will understand what is, how it works, where to apply and how to code your own Deep Belief Network.
Here is an overview of the points we are going to address:
In this article, we are going to discuss the benefits, challenges and ethics of automation at scale.
Last December the world celebrated and we all were anxious to get started with 2020, as a new year comes with the promise of better fortune but little did we all know that we would be in this situation. Highly social beings being forced to keep social distance, wear masks and develop hygiene routines better than hypochondriac — basically going against our nature.
I would like to take a moment to express my condolences to the families who lost their loved ones to…
Rethinking the future we want not the one that will befall us. We are in charge of our destiny.
Nowadays there are a lot of headlines saying things like: “AI to run a factory X or AI is going to replace human cooks in this country”.
I strongly believe that we should stop and think before automating away everything there is. In the wise words of Richard Koch:
“The road to hell is paved with the pursuit of volume. Business is wasteful, because complexity and waste feed on each other, a simple business will always be better than a complex.”
In this article, we are going to talk about and implement image-to-image translation for Super-Resolution. Furthermore, talk about and add the best tips tricks to improve our results.
I started my Data Science journey with a fascination for the raw data processing power and potential for doing good with the insights gained from the data. In this day and age, we are producing more data than any living organism can process, so we have to turn to our own creations to help us in this regard. More than that, this field is the key to the project of my life…
In this article and the following, we will take a close look at two computer vision subfields: Image Segmentation and Image Super-Resolution. Two very fascinating fields.
Two years ago after I had finished the Andrew NG course I came across one of the most interesting papers I have read on segmentation(at the time) entitled BiSeNet(Bilateral Segmentation Network) which in turn served as a starting point for this blog to grow because of a lot of you, my viewers were also fascinated and interested in the topic of semantic segmentation.
More we understand something, less complicated it becomes.
A future filled with unseen proportions creativity with Man and Machine work together to reach new heights.
This has been an amazing journey of discovery and creativity.
Exactly one year ago I was on the phone with my friend and he was pitching me one of his ideas that triggered in me a side that I for some reason buried it deep. …
In this article, we are going to take a look at Generative Adversarial Networks(GANs), specifically Wasserstein GAN.
We are very inquisitive beings, through our curiosity and the ability processing some part of the crazy amounts data that come every single second from the various sensory input forms, visual, audio, tactile and etc. we are able to put use all that data or at least a good part of it and generate new data in form of thoughts, dreams and imagination.
With that in mind, “can we teach machines to create data from data?”
If we actually stop to think about…
In this article, we will learn about GNNs and its structure as well as its applications
Deep learning has changed the way we process data using the increasing computational “cheap” power(Moore’s law) to solve real-world problems and accomplish some cognitive tasks that our brains do almost effortlessly such as image classification, natural language processing, vídeo processing and etc.
But most of these tasks have data or structure that is typically represented in the Euclidean space. …
In this article, we are going to address one of the biggest questions in Deep Learning: “Is deeper always better? If so, when is it Deep enough”
Yes, going deep has its benefits, yet is it always the best solution?
The answer is a big “NO”. Going deep has its limitations because there are parts of the problem that will remain untouched. If we want to improve and push forward Deep Learning for Computer Vision we will definitely need to do better than just going deep.
The story of why there is no such thing as full-stack AI because you can’t know it all.
Disclaimer: This not going to be your usual article.
AI is a very elusive and interesting field, uncovering the wonders of what computer vision classification network can do with a superhuman degree of accuracy or the elaborate text that language model can generate or understand the semantic meaning of, is fascinating, but truth be told you can’t know it all.
I have been fighting with this dilemma for a while now.
Not sure I can call it shiny object syndrome because most…
Computer Engineering Student, Web Dev. & AI/ML dev