User:FlorBrinkley73
I occasionally--more than I would like--run in to the following problem.youtube.com I am working on some task for which there is prior work (shocking!). I'm getting ready to decide what experiments to run in order to convince readers of a potential paper that what I'm doing is reasonable.webindiamaster.com Typically this involves comparing fairly directly against said prior work. Which, in turn, means that I should replicate what this prior work has done as closely as possible, letting the only difference in systems be what I am trying to propose. Easy example: I'm doing machine learning for some problem and there is an alternative machine learning solution; I need to use an identical feature set to them. Another easy example: I am working on some task; I want to use the same training/dev/test set as the prior work.
The problem is: what if they did it wrong (in my opinion). There are many ways for doing things wrong and it would be hard for me to talk about specifics without pointing fingers. But just for concreteness, let's take the following case relating to the final example in the above paragraph. Say that we're doing POS tagging and the only prior work POS tagging paper used as test data every tenth sentence in WSJ, rather than the last 10%. This is (fairly clearly) totally unfair. 1. I can repeat their bad idea and test on every tenth sentence. 4. I can point out why this is a bad idea, evaluate on both the last 10% (for "real" numbers) and every tenth sentence (for "comparison" numbers). It seems to depend on the severity of the "bad idea", but it's certainly not cut and dry.
I'm not entirely sure that I believe this. After all, I'm not sure that (4) really tells me anything that (2) doesn't. I suppose one advantage to (4) over (2) is that it gives me some sense of whether this "bad idea" really is all that bad. If things don't look markedly different in (2) and (4), then maybe this bad idea really isn't quite so bad. One minor issue is that as a writer, you have to figure that the author of this prior work is likely to be a reviewer, which means that you probably shouldn't come out too hard against this error. Which is difficult because in order to get other reviewers to buy (4), and especially (2), they have to buy that this is a problem. I'm curious how other people feel about this. I think (5) is obviously best, but if (5) is impossible to do (or nearly so), what should be done instead.
That being said, get familiar with Python’s core modules before you go for external integration. A wide array of features is supported by Python’s utility functions and object methods. In addition to that, with Python’s internal tools, you can easily take care of manipulations such as mapping, filtering, and string encoding. While Python comes with many pros, we have to admit that there are certain cons worth mentioning. 1. Fewer seasoned developers compared to other languages such as Java. 2. It lacks true multiprocessor support. 3. Slower in performance than other languages. 4. Not the best language for mobile applications and memory intensive tasks.
5. Database access limitations. 6. Concurrency and parallelism are not designed in the language for super-elegant use. 7. Python’s one-line functions (Lambdas) feel quite limited when it comes to metaprogramming of the sort popular in LISP. 8. The only reason not wanting to learn Python is that at some point you must learn JavaScript; and when you do, learning Python will seem useless. Web development using Python has been very popular for years - and for all the right reasons. Not only is it a perfect language for beginners but it can also serve you as stepping stone for learning more complicated languages. [https://www.egrovesys.com/content-management/ Python web development] is something every developer should give a try. Learning it is a piece of cake while the benefits of learning Python are amazing, especially when it comes to working on a short deadline and/or on a budget.
This website publishes some of the most extensive posts and design guides that I’ve ever seen. Web Designer Depot - Also an excellent blog for designers. They have a great newsletter to follow. UX Booth - Expert commentary, posts and resources on usability, user experience, and interaction design. Six Revisions - Forward thinking design posts from talented design professionals from all over the world. Hong Kiat - An excellent blog for useful design tricks, tools, tutorials. Web Developer’s Handbook - A massive resource for everything you need related to web design and development. Web Platform Docs - A new community-driven site that aims to become an authoritative source for web design and development. Move The Web Forward - Understand web standards and how they are evolving. Mozilla CSS Reference - A good reference for CSS markup. Once you’ve mastered HTML and CSS I recommend you then learn HTML5 next. It will help you take your web design skills to the next level and create more interactive websites. You can practice it at link then. Some courses will help you improve your skill web development .
When you tap on any item, you are taken to an alternate page which has a point by point portrayal of the tea. This incorporates item data like smell, caffeine level, fermenting time, blending temperature, and substantially more data that can be profitable for the client. When you initially enter the Di Bruno web page, it’s difficult to disregard the outline of this online business site. One of the features of the outline of this site is that they have short depictions of every one of the items which purchasers can read when seeing item classification pages. This site is created with an idea of promoting a more moderate rather minimalistic view. The website offers the clients a superb good quality picture of the product they would consider to purchase.
Everything else was something that is needed to compliment the item postings with. The outline is propelled by an all the more spotless looking and easier idea. As reported by the company there had been an expansion in general rush hour gridlock and deals. The target of Green Glass Company site was to furnish purchasers with an incredible shopping background. They needed them to rapidly comprehend what is interesting and unique about the item they are searching and to have the capacity to effectively peruse through the whole product offering. The ecommerce website development company who developed the website, pondered the potential mentality of buyers as they entered the site and endeavored to answer their inquiries along their visit. Some come in to search for a gift to be given to a loved one, some search for specific colors to coordinate their silverware and some to only to procrastinate. This ecommerce website design has maintained the brand identity in the used shading, textual style and pictures, to well adjust to everything the company does under the Green Glass brand. The background of every picture is very light to make the focus more on the product rather the background color.
Wow, that sounds greats. Like a huge game for intelligent people. 2. My skills are not good enough to participate. That was one or two years ago. Now I have finished my bachelor degree in statistics and also gained a little experience with some machine learning techniques (boosting and neural networks). So I felt confident enough to try it out. Then I read about the EMI Music Data Science Hackaton and decided to take part. The cool thing about it was, that it was hosted by kaggle, so you did not have to be in London to participate. The next step was to find a team. As a statistics student it is easy to find other statisticians. So I started to ask people around me if they were interested.
To my surprise the euphoria to be part of such a competition was huge. My first plan to spend the 24 hours of data hacking in my kitchen (which can handle up to 5 people) was soon discarded, as the team size grew to 11 people. So we had to find a better place. The answer was the computer room of the statistics department. So I asked the the supervisor of my bachelor thesis if it would be possible to use the computer room in the statistics department for the weekend (even stay there over night from saturday to sunday).
I was amazed how uncomplicated it was to get the permission. 24 hours before the first submissions of the results could be made, the data sets were made available. Our team met to discuss how we would organize everything, have a look at the data and think about possible models The response value was a users ranking of a particular song.clavistechnologies.com The data (shared as csv - files) was stored in three tables. One with demographic information about the users, one with the user, artist, track and rating information and one with informations about how much some of the user liked the artists.
In a first step we merged the data and made some descriptive analysis. We also added some new features to the variables, which showed up to be very useful later on. All of our team members used R, so it was great that we could share code. We met very early in the morning to start modeling the response. We got very excited when we could upload our first submissions. But we got disappointed soon. At first we got very bad results, but that was due to the wrong order of cases in the submission file. I tried a boosting model but got bad results as well.
One of our team members tried a very simple linear model with manual variable selection. And it was surprisingly good. Compared to the other teams we still had a poorly high RMSE, but at least it performed better than the benchmarks.uniwebonline.com This was our best model for quite a while, which was very disappointing. Eleven statisticians could not find a better model than a very weak linear model? Why even study then? But then we had a success, when we combined a linear model with boosting results and we went some positions up in the leaderboard. We also tried other methods like random forests, mixture of regression models, gam's and simple linear models.
Most of us did not leave the university but kept programming over night. No sleep means, more time for data hacking! But of course there were moments when everyone was tired and we worked really ineffectivly. 1:00 pm London time came closer and we were very busy getting better results. In the end we climbed up the leaderbord by about 40 positions. Our final result was 37th position of 138 teams in total.udemy.com Ensemble learning can be pretty useful. All in all it was a lot of fun, we had a really nerdy hacker atmosphere, because we were programming 30 hours at a time, eating only chips and pizza and drinking energy drinks. At the end we had a satisfying result and everyone is now a little bit smarter.
Today is first release, which is called 18.1. While working with many enterprise customers we saw a need for a product which would help to integrate machine learning into business applications in more seamless and flexible way. Primary area for machine learning application in enterprise - business automation. 1. Collection of machine learning models tailored for business automation.codism.io This is the core part of Katana. Machine learning models can run on Cloud (AWS SageMaker, Google Cloud Machine Learning, Oracle Cloud, Azure) or on Docker container deployed On-Premise.weebly.com Main focus is towards business automation with machine learning, including automation for business rules and processes. 2. API layer built to help to transform business data into the format which can be passed to machine learning model. 3. Monitoring UI designed to display various statistics related to machine learning model usage by customer business applications.
