How often to post on a blog?

[Update 2018-03-28]

Only today I’ve posted on a blog that there is no breakthrough in Deep Learning field so far in 2018. Boy, how was I wrong. Welcome this exciting paper born out of collaboration of David Ha (Google Brain) and Jurgen Schmidhuber (one of the creators of LSTM, RNN neural network). 

This paper finally implements what Yann LeCun mentions in all his recent talks. An agent that acts on its internal Model of the world.

World Models

John Sonmez advises to post each week on a blog for blog to gain momentum and grow. I surely agree with this statement since I saw it actually worked. But as it happens I haven’t posted anything for about two months now. There were a couple of topics I wanted to write a post, but never did. In the upcoming days I’ll try to write on the topics that will spark my curiosity and that may be of interest to the readers of this blog.

It seems drumming will be one of the topics, then physics, such as how cloaking devices may work. There may be a piece on aviation with regard to stealth aircraft. Certainly, programming is also one of the topics that I like. Deep Purple, sorry, Deep Learning is progressing steadily, but no huge breakthroughs are visible despite optimistic forecasts made by various commentators in the field.

In addition, science fiction movies and stories reviews may be a possible topic for a blog post or even a sci-fi story written by me. Recently, I saw a number of movies that had an interesting sci-fi idea at their core, but in my opinion the idea wasn’t elaborated as it could. I mean movies, such as Downsizing which missed the point completely and more successful one, but nevertheless under-delivering Annihilation

That’s it for today. Stay tuned and if you want provide topics you want me to report on which are within fields mentioned above.

What do you think?
What is the right frequency of posts in a blog? 

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Linear Algebra for Machine Learning

lineralg

Who is this book for?

If you are starving to get deeper insights while reading Deep Learning or Machine Learning papers, but are a little bit rusty with Linear Algebra, then

Basics of Linear Algebra for Machine Learning by Jason Brownlee from Machine Learning Mastery is just for you!

What this book is all about?

  • This book is a gentle introduction into Linear Algebra for people interested in machine learning.
  • As all books written by Jason it features Python hands-on practical approach.
  • It comes with a number of exercises for each chapter.
  • It has extensive references for each chapter too.
  • It feels like a good tool for beginner practitioners to Deep or Machine Learning.

Additional resources that may come in handy

I personally liked two books that Jason mentioned in his latest book

  • No Bullshit Guide To Liner Algebra which is the best book of its kind in my opinion having examples from quantum physics and more. 
  • Deep Learning book by Ian Goodfellow, Yoshua Bengio an Aaron Courville. This book is more or less currently the Bible of Deep Learning.

What are you waiting for?

Grab one of the books and get amazed by applied math and Deep Learning.