Skip to main content

deep learning 1

in deep learning you don't have to do feature selection, this is automated by the deep learning algorithm by looking at the patterns of the data. we just feed it in and see the output classification.

Talking about automation, this is a time saver for human being isn't it? The point why computer is really helpful to us is the fact that it could automate task. Now, do we need to understand how the machine do it?

I think this is just a matter of trust or fear from humanity to machine. We don't need to understand anything, but sometimes what it matters is solving the problem.

Like a doctor giving the medicine to a patient, granted he or she will have the understanding of how the medicine works into human body. This understanding however is 'limited', the doctor kind of 'trust' the work of the people who created those medicine that it will do no harm to the body. But the truth of the matter is it does not work always that way. There is a degree of 'blackbox' in the medicine of how it will effect the body, cells, etc. So, in this case the doctor might have an 'overview' understanding of how the medicine will works but at the same-time there is a degree of 'unknown'.

So, this bring me back to the idea of deep learning being able to automate the feature selection process that normally we human do it manually or the need of having a domain expertise.

I think this is why deep learning is so powerful because the computer does what it does best, which is sequential computational processing that is no match with human being. It could see patterns, particularly the bigger the data the better it is for machine to do this ...and they have the superpower in processing big chunks of data in a sequential manner.

So, we have to make a jump to deep learning for this reason and kind of make the classical manual feature selection algorithm kind of obsolete? I don't know the answer to that question, only time will tell but my intuition said 'yes'!

I believe as of now there is still space of classical machine learning especially if the data does not involved of huge amount of data. In this case vision problems specifically would be in the realm of deep learning because of its sheer amount of data.


Popular posts from this blog

Very Old Youtube video ... babbling about myself having mid-life crisis

Enjoy :)

why I put all or most my money into technology sector

Technology role is to move something from scarcity to abundance. Everyone wants it. Who wants to live in scarcity? Think about all the technology that we use today: Apple, Amazon, smartphone, TV, facebook, social media, netflix, etc. You might think that technology also 'create' loneliness, well technology is a tool just like a knife we could use them to kill ourselves or use them to make a delicious dish. Besides what is the other option? Technology is a creation of wealth, that market/people could use and enjoy it. But on the other hand creation is also destruction . Why is that? Everytime we create something that the market value, it will destroy something that the 'old' market value. It is just 2 sides of the same coin, we could not avoid it. But on the aggregate what we create is more positive than we 'destroy'. Look at the revolution from agriculture to industrial age. what do we create? thousands or millions of job to the machinist, people who oper

My investment portfolio in robinhood

I have been using this #robinhood app probably the last 2 years, love it. The UI is amazing and love the mission of the company which is to democratize financial stocks for everyone. Feel free to comment and argue nicely :)