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Free download. Book file PDF easily for everyone and every device. You can download and read online Kreating Truth (Episode One) file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Kreating Truth (Episode One) book. Happy reading Kreating Truth (Episode One) Bookeveryone. Download file Free Book PDF Kreating Truth (Episode One) at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Kreating Truth (Episode One) Pocket Guide.

As a practicing Data Science-professionals, in contrast to the people from academy and science, we should and will solve real business problems in our work. In any competition, most of the work was already completed for us by the sponsors. We already have a business objective, the selected approximating metric, collected data, and our task is to build a working pipeline out of all of these. Basically, all the newcomers come to Kaggle to improve their practical experience, but do not forget that, in addition, there are at least two additional purposes:.

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This way you will avoid pain, frustration, and resentment for this unjust world. Usually, this method leads to hard overfitting on the public leaderboard but sometimes allows you to get almost silver. The author, at the initial stage, does not recommend this approach read below about the belt and the pants. And also the opinion from Valeriy Babushkin venheads :. Kaggle Competition Master is an excellent proxy metric for assessing the future team member. People who have achieved the title of master, with high probability, are able to write at least an average quality code, well versed in machine learning, are able to clean the data and to build sustainable solutions.

And if solution he contributed with was something more than a public kernel which is quite easy to check , it is a good chance for a specific conversation about technical details that is much better and more interesting than the classical job interview for a fresh grad which provides much less information of how people in the future will succeed in the job. The only thing we have to fear, and what I came across is that some people think that Data Science work is the same as at Kaggle, which is totally not true.

Let me explain. Practically in every competition closer to its termination somebody writes the public kernel with a solution that shifts the entire leaderboard up, but you, with your solution, down. And every time at the forum The Pain Starts! Remember that Kaggle is the competitive Data Science and your place on the leaderboard depends only on you. Not from the guy that posted the kernel, not from the stars that came together or not, just on how much effort you put into the decision and whether you used all the possible ways to improve it.

Seriously, the public kernel with a better solution than yours means that you have missed something in your own pipeline. Locate it, improve it and go around all the hamsters with the same score. Remember that to return to the place you just have to be a little better than this public kernel. Just how I was upset by this moment in the first competition! My hands were falling, and I was ready to give up. A moment ago you were in silver, and here you are now at… the bottom of the leaderboard. Moreover, this moment will be present only in the early phase of your competitive process.


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The more experienced you become, the less you will feel the influence of the published kernel and the stars. That means that all useful signal, which was pulled from the data by that kernel was already pulled by our models. My recommendation is python 3. Python has already become the de facto standard in Data Science, given a large number of libraries and community. Ubuntu is easy itself, plus the part of data processing is sometimes more natural to do in bash instead of python. And your life will become much more comfortable and more pleasant. Once your pipeline becomes more or less stable, I recommend moving your code to the individual modules.

There is the opposite approach where the participants are trying to use jupyter notebook as infrequently as possible and only when necessary, preferring just to write pipeline by scripts.


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  8. The adept of this option is, for example, Vladimir Iglovikov ternaus. And there are those who are trying to combine jupyter with any IDE, such as Pycharm. Each approach has the right to exist, and each has its pros and cons, as they say, tastes differ. Choose what you are comfortable to work with. But under any scenario remember the rule. Is crucial for the further ensemble of several solutions.

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    Well, there are at least three options:. In General, in the community, there is a tendency of gradual transition to the third option, because the first and the second ones have their drawbacks, but they are simple, reliable and, honestly, for Kaggle, they are more than enough. A few more words about python for those who is not a programmer. Your task is to understand the basic code structure and the basic essence of language. On the Internet there are many excellent courses for beginners, maybe in the comments, somebody will give you the links.

    I am not so good at working with imagery data. The only attempt to do it was in the competition Camera Identification , in which, by the way, the teams with the tag [ods. In General, if you want to start working with images, then you need other frameworks and other guides. In public kernels, all these tasks are usually gathered in one code, but I highly recommend to create a separate notebook and a separate module or set of modules for each of these subtasks.

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    This approach will help you later. In fact, a lot still depends on the amount of data. In the TalkingData competition, for example, I had to go through memmap to avoid out of memory error when creating the dataset for lgb. Pilots are the tip of the spear in the television industry. This is why so much time, energy, and money goes into making these pilots. I hope it never happens to you. The first step in getting a greenlight is selling your pitch. This entails you going into a room with studio execs or network execs or both and sketching out the world you imagine for your show.

    If the execs like what they hear, they buy your pitch. If the pitch was a sketch of your idea, then the script is the painting: colored in, fully formed and framed. That was then, though.

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    Now executives give notes based on their perspectives as the network and studio, e. Which can be tricky. You, meanwhile, get to spend your break developing acute heartburn as you wait for news on your greenlight. Happy holidays indeed! Christmas came late: the network decided to pick up your script and order it to pilot.

    Again, no time to celebrate, because you need to get the ball rolling on making your pilot. After getting greenlit, the next step for creatives working on pilots is to put together their team director, casting director, possibly a showrunner, etc. Unfortunately, most of Hollywood is doing that same exact thing at the exact same time.

    It becomes a race to get your first, second, or even third choice for roles, while Business Affairs negotiates contracts for all your players all while they negotiate with other parties as well. Since statistically most shows fail and lose money , networks and studios look for ways to minimize their downside.

    After putting your team together and finding a network-approved cast, you have to film your pilot. The length of a shoot depends on the show. Multicam comedy pilots shot on a sound stage may take only a few days; single-cam pilots, on the other hand, may shoot for weeks or more for example, the massive 2-hour LOST pilot took two-and-a-half months to shoot. Even so, his personal struggles and weight gain have continued to serve as the basis for drama on the series. Getty Images. Scott Disick A regular on "Keeping Up With the Kardashians," Kourtney's ex-boyfriend and father of her three children, Scott Disick, became known for his hot temper and substance abuse issues.

    But the marriage did not last long: The couple split just 72 days later. View In Gallery. Show Comments.