why would every ML guide use cats as example
Machine Learning Guides
11–20 of 56 posts
Re: Machine Learning Guides
#12Daily reminder for data scientist and machine learning types: fill your pockets while you can, because machine learning bootcamps are on the horizon!
There's been data science/machine learning bootcamps around for awhile (Galvanize/Metis being common examples in San Francisco), but apparently job placement is not in a good place (as with normal bootcamps). Indeed Machine Learning/Deep Learning has become much more accessible thanks to the number of free guides such as this. But that means data science job placement will become more difficult as competition increas…
The issues I've heard from a few people in hiring is that there is a surplus of junior data scientists from these camps and a shortage of senior data scientists to manage them. Problems not dissimilar to tech hiring in general, but companies need a lot more SWEs than data scientists.
Re: Machine Learning Guides
#13Unlike most guides I've seen about ML, this one does a good job of focusing on developing and deploying a simple model first, then iterating. There are also lot of practical tips here, especially around feature engineering.
> the second phase of machine learning involves pulling in as many features as possible and combining them in intuitive ways. During this phase, all of the metrics should still be rising
As Google points out, after you build an initial model, the next step to increase accuracy is to perform feature engineering. They explain that this can be done manually or automatically using something like deep learning. Another option that people here might consider is using a library like Featuretools (https://github.com/featuretools/featuretools) for "automated feature engineering". Note: I am one of the developers.
Our goal is to help you increase the performance of your models without sacrificing the interoperability of your features. We have a post up about how our algorithm works here: https://www.featurelabs.com/blog/deep-feature-synthesis/. There are also plenty of real world demos on our website: https://www.featuretools.com/demos
Re: Machine Learning Guides
#14Deep learning and blockchain noise seems to be dying out slowly. Are we going downhill on the hype curve, is it time to short those buzzwords?
I think most people just don't understand either very well. They feel the hype but are waiting for others to show them examples of what to do.
Re: Machine Learning Guides
#15Daily reminder for data scientist and machine learning types: fill your pockets while you can, because machine learning bootcamps are on the horizon!
There's been data science/machine learning bootcamps around for awhile (Galvanize/Metis being common examples in San Francisco), but apparently job placement is not in a good place (as with normal bootcamps). Indeed Machine Learning/Deep Learning has become much more accessible thanks to the number of free guides such as this. But that means data science job placement will become more difficult as competition increas…
Depends what the company is doing.
Most companies are going to utilise ML to some extent. Once technology and tooling improves they'll need boots on the ground engineers and not labs with R&D teams
Re: Machine Learning Guides
#16Daily reminder for data scientist and machine learning types: fill your pockets while you can, because machine learning bootcamps are on the horizon!
Have boot camps noticeably suppressed wages for software engineering in general?
Re: Machine Learning Guides
#17Deep learning and blockchain noise seems to be dying out slowly. Are we going downhill on the hype curve, is it time to short those buzzwords?
Deep learning is far from slow down. The research speed has increased a lot in the last 5 years. It amazes me how fast the researchers are able to come up with new things. From one year to the next one there is something shiny and new.
I don't follow very much blockchain but seems like it was 6 years ago
Re: Machine Learning Guides
#18Daily reminder for data scientist and machine learning types: fill your pockets while you can, because machine learning bootcamps are on the horizon!
You cannot learn machine learning or deep learning in a few months. You can learn to copy what these guides do, but if you want to do something slightly different you will feel you know nothing (because you actually probably don't know anything about the maths behind why the things works, so when you want to change them you don't know how)
Re: Machine Learning Guides
#19Perhaps it's somewhat off-topic, but I've built a spam detector similar to the article's withOUT using "direct" AI, but rather via a key-word or key-phrase "ranker". A simplified example is given below.
The advantage over other techniques is that one can easily trace the exact math of a conclusion, and tune it as needed. The disadvantage is that one probably has to manually tune it all rather than let the machine "learn". However, a hybrid approach could be used whereby "pure" AI suggests words and phrases to encode.
rule.addList("nigerian, prince", rank=7);
rule.addPhrase("great opportunity", rank=5);
rule.addPhrase("lisa smith", rank = -4); // probably good
Re: Machine Learning Guides
#20Earlier quoted context omitted.
There's been data science/machine learning bootcamps around for awhile (Galvanize/Metis being common examples in San Francisco), but apparently job placement is not in a good place (as with normal bootcamps). Indeed Machine Learning/Deep Learning has become much more accessible thanks to the number of free guides such as this. But that means data science job placement will become more difficult as competition increas…
Depends what the company is doing. Most companies are going to utilise ML to some extent. Once technology and tooling improves they'll need boots on the ground engineers and not labs with R&D teams
Masters/PhDs can be boots-on-the-ground engineers too.