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I will read several research papers and I will write summaries here in a way a 15 year would understand.This might sound like blah blah blah...But I strongly believe it's a good start.

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Why?

Most of the people on internet only go through the blogs and tutorials. That is a great way but I feel like "Survey papers" lay the good big picture of the field/concepts/technology.

When a 15 year old cares more about the research papers and research methodology , then the critical thinking skills will start to serve the society. Reading research papers are only sure way to increase the maturity required to do the research

$ give me critical-thinking powers

Email : KKhanal16@winona.edu for further discussion

I will try to read research paper on this sequence

The research paper that introduced concepts are used in modern industry will be covered more than older research papers

Since, I already had a University level Machine learning and Statistical learning course, I have more background to start reading machine learning but that doesn't mean you shouldn't start reading research paper. This is why i usually keep it non math, more intuitive style. Being a Math major too, I struggle with all this math. But the end goal of AI is intuition. Every fancy Math theorem, tricks are used so that AI/program will behave like a rational human. I believe starting with "summary" of research papers and grabbing a good book that covers the most of those research paper will formally get you into ML research community. This is what i think and that's why i started this "Book"

Sridhar MahadevanSridhar Mahadevan, Fellow of AAAI says in one of the Quora post,

Do yourself a favor, and pull back, at least a little. Don’t spend every waking hour hacking TensorFlow or PyTorch, no matter how tempting these may be. Learn AI! Understand the large number of subfields, and what people in these subfields do. *Read the classic papers***. Understand the “frame problem”, what is the “no-free-lunch “ theorem in ML, what “behavior-based robotics” is all about, what the major issues in natural language processing are, what “random projections” are, and so on. There is an ocean of work in AI that is not DL, and it is every bit as exciting as DL is. And then, yes, if you must, certainly learn TF or PT. :-)

Most of the ideas in recent machine learning advancements has its origin and inspiration on theories developed in past.

Nothing on these papers are my work. All credits are to authors of those papers.I am reading those and write as i understood. So, it's always wise decision to read those original works. These summaries are like fast food. Quick but i don't guarantee wellbeing of health.

I would like to thank these authors for doing these wonderful work for humanity!

Link for top papers : https://scholar.google.com/citations?hl=en&view_op=top_venues&vq=eng_artificialintelligence

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