They are like basic arithmetic - we use it every day, without even thinking. Most users didn't even open the main page.ĭespite the popularity, classical approaches are so natural that you could easily explain them to a toddler. I heard stories of the teams spending a year on a new recommendation algorithm for their e-commerce website, before discovering that 99% of traffic came from search engines. But when you are small, it doesn't make sense. For them, 2% accuracy is an additional 2 billion in revenue. When you see a list of articles to "read next" or your bank blocks your card at random gas station in the middle of nowhere, most likely it's the work of one of those little guys.īig tech companies are huge fans of neural networks. Nowadays, half of the Internet is working on these algorithms. They solved formal math tasks - searching for patterns in numbers, evaluating the proximity of data points, and calculating vectors' directions. The first methods came from pure statistics in the '50s. Dear media, it's compromising your reputation a lot. That's why the phrase "will neural nets replace machine learning" sounds like "will the wheels replace cars". The general rule is to compare things on the same level. To not look like a dumbass, it's better just name the type of network and avoid buzzwords. Nowadays in practice, no one separates deep learning from the "ordinary networks". A popular one, but there are other good guys in the class.ĭeep Learning is a modern method of building, training, and using neural networks. Neural Networks are one of machine learning types. Machine Learning is a part of artificial intelligence. So don't pay too much attention to the percentage of accuracy, try to acquire more data first.Īrtificial intelligence is the name of a whole knowledge field, similar to biology or chemistry. Sometimes it's referred as "garbage in – garbage out". There is one important nuance though: if the data is crappy, even the best algorithm won't help. The method you choose affects the precision, performance, and size of the final model. Please, avoid being human.Īlgorithms Most obvious part. They choose only features they like or find "more important". That's why selecting the right features usually takes way longer than all the other ML parts. But what are they if you have 100 Gb of cat pics? We cannot consider each pixel as a feature. When data stored in tables it's simple - features are column names. In other words, these are the factors for a machine to look at. Those could be car mileage, user's gender, stock price, word frequency in the text. They are so important that companies may even reveal their algorithms, but rarely datasets.įeatures Also known as parameters or variables. It's extremely tough to collect a good collection of data (usually called a dataset). In their place, I'd start to show captcha more and more. Remember ReCaptcha which forces you to "Select all street signs"? That's exactly what they're doing. Some smart asses like Google use their own customers to label data for them for free. Manually collected data contains far fewer errors but takes more time to collect - that makes it more expensive in general.Īutomatic approach is cheaper - you're gathering everything you can find and hope for the best. There are two main ways to get the data - manual and automatic. The most exciting thing is that the machine copes with this task much better than a real person does when carefully analyzing all the dependencies in their mind. Let's provide the machine some data and ask it to find all hidden patterns related to price.Īaaand it works. People are dumb and lazy – we need robots to do the maths for them. An average Billy can't keep all that data in his head while calculating the price. The problem is, they have different manufacturing dates, dozens of options, technical condition, seasonal demand spikes, and god only knows how many more hidden factors. Yeah, it would be nice to have a simple formula for every problem in the world. People do it all the time, when trying to estimate a reasonable cost for a used iPhone on eBay or figure out how many ribs to buy for a BBQ party. In machine learning terms, Billy invented regression – he predicted a value (price) based on known historical data. He went over dozens of ads on the internet and learned that new cars are around $20,000, used year-old ones are $19,000, 2-year old are $18,000 and so on.īilly, our brilliant analytic, starts seeing a pattern: so, the car price depends on its age and drops $1,000 every year, but won't get lower than $10,000. He tries to calculate how much he needs to save monthly for that.
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