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Main challenges of Machine Learning

Latest reply: Dec 18, 2021 16:23:31 717 26 9 0 0

Main challenges of Machine Learning

    We are living a wonderful era of Machine Learning, dealing enjoying with machine learning, deep learning powered applications.

    One of the keys that made us arrived to this stage is 4 letter word “data”. For better performance, those applications rely on huge quantity of data that’s is generated every day.

    There is a real competition between internet compagnies  even countries on which can gather more data the new oil.As Dr Kai Fu Lee said, China is the new Saudi Arabia.

    Nevertheless, everything is not just fine ready to work, there are still difficulties to be solved.

    The core difficulty is the Insufficient quantity of training data.

For a child to learn to identify an Elephant, the kid just need a few samples of pictures or drawings of that animal. That’s not the case for a computer. For a computer to get close precision to the child’s performance at identifying elephants, it, needs millions of training samples. This huge quantity of data is not always available and may  cost a lot to gather them.

In 2001 Microsoft’s researchers Michele Banko and Eric Brill showed very different Machine Learning algorithms, including very simple ones, performed almost identically well on complex problem of natural language disambiguation once they were given enough data.

 

accuracy

 

    In addition, sampling problem is another data related challenge to Machine Learning.

The US  presidential election ( Landon vs Roosevelt ) in 1936 is the best example to illustrate this problem. The literary Digest conducted a very large poll, sending mail to about 10 million people. They got 2.4 million answers, and predicted with high confidence that London would get 57 % of the votes. But Roosevelt won the election with 62% of the votes. There is a reason to that. To obtain the addresses to send the polls, the Literary Digest used telephone directories, list of magazine subscribers, club membership lists. All these lists tended to favour wealthier people wo were more likely to vote Republican hence Landon

    Another challenge is Poor Quality data. Poor quality data is a data sets with a huge quantity of outliers, missing data labels, duplicated items, incorrect format samples…

    Those above-mentioned challenges can really low the performance of our model. I think that’s why there is a new approach being developed which is the Data centric approach. This new way sof dealing with ML problems focus more on improving data quality rather than the code only.

 


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little_fish
Admin Created Jul 1, 2021 01:26:44

Thanks.
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csk99
csk99 Created Jul 1, 2021 06:34:49 (0) (0)
 
azkasaqib
azkasaqib Created Jul 18, 2021 17:48:32 (0) (0)
 
Vlada85
MVE Author Created Jul 1, 2021 01:38:06

Thank you for sharing
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csk99
csk99 Created Jul 1, 2021 06:35:03 (0) (0)
 
andersoncf1
MVE Author Created Jul 1, 2021 01:56:44

Thanks for sharing
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csk99
csk99 Created Jul 1, 2021 06:35:12 (0) (0)
 
olive.zhao
Admin Created Jul 1, 2021 09:35:43

Good!
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csk99
csk99 Created Jul 1, 2021 16:26:26 (0) (0)
 
azkasaqib
azkasaqib Created Jul 18, 2021 17:48:39 (0) (0)
 
Unicef
MVE Created Jul 1, 2021 14:30:44

NICE
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csk99
csk99 Created Jul 1, 2021 16:26:19 (0) (0)
 
kita
Created Jul 4, 2021 10:24:24

Great share
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csk99
csk99 Created Jul 6, 2021 05:48:02 (0) (0)
Thank you  
LilStylz237
Moderator Created Jul 5, 2021 19:41:10

Very good
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NTan33
Created Jul 7, 2021 01:16:14

Truly, the data used is the key component to ML.
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csk99
csk99 Created Jul 19, 2021 06:48:30 (0) (0)
 
MahMush
Moderator Author Created Jul 7, 2021 04:22:42

Some of the challenges.

1. Data Collection · 2. Less Amount of Training Data · 3. Non-representative Training Data · 4. Poor Quality of Data · 5. Irrelevant/Unwanted Features ·
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csk99
csk99 Created Jul 19, 2021 06:49:07 (0) (0)
Thanks for your contribution  
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