23 Deep Learning Papers To Get You Started — Part 1 (Reading Slowly)

23 Deep Learning Papers To Get You Started — Part 1 by Rupak Kr. Thakur.

Deep Learning has probably been the single-most discussed topic in the academia and industry in recent times. Today, it is no longer exclusive to an elite group of scientists. Its widespread applications warrants that people from all disciplines have an understanding of the underlying concepts, so as to be able to better apply these techniques in their field of work. As a result of which, MOOCs, certifications and bootcamps have flourished. People have generally preferred the hands-on learning experiences. However, there is a considerable population who still give in to the charm of learning the subject the traditional way — through research papers.

Reading research papers can be pretty time-consuming, especially since there are hordes of publications available nowadays, as Andrew Ng said at an AI conference, recently, along with encouraging people to use the existing research output to build truly transformative solutions across industries.

In this series of blog posts, I’ll try to condense the learnings from some really important papers into 15–20 min reads, without missing out on any key formulas or explanations. The blog posts are written, keeping in mind the people, who want to learn basic concepts and applications of deep learning, but can’t spend too much time scouring through the vast literature available. Each part of the blog will broadly cater to a theme and will introduce related key papers, along with suggesting some great papers for additional reading.

In the first part, we’ll explore papers related to CNNs — an important network architecture in deep learning. Let’s get started!

The start of what promises to be a great series on deep learning!

While the posts will extract the concepts and important points of the papers, I suggest you download the papers and map the summaries back to the papers themselves.

It will be good practice on reading original research, not to mention re-enforcing what you have learned from the posts.

In my reading, I will be looking for ways to influence deep learning towards one answer or another.

Whatever they may say about “facts” in public, no sane client asks for advice without an opinion on the range of acceptable answers.

Imagine you found ISIS content on Twitter has no measurable impact on ISIS recruiting. Would any intelligence agency would ask you for deep learning services again?

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