Щедных владимир: ВЛАДИМИР реквизиты — ИНН 5507011190, ОГРН 1065507042061

Во Владимире вот уже год действует уникальное творческое пространство

+ A —

Старая фабрика на новый лад

О, сколько нам открытий чудных готовит прогулка по родному Владимиру! Сегодняшним открытием стало творческое пространство «Фабрика», спрятавшееся от местных и от туристов на улице Сакко-и-Ванцетти, 50.

Больше ста лет назад здесь был свечной завод, потом много лет подряд в здании находилась фабрика музыкальных инструментов, в 90-е территория пришла в упадок. Потом здесь располагались различные производства, и только три года назад, после проведения реструктуризации, у собственника, владимирской бизнесвумен Анны Жуковой возникла идея дать фабрике второе рождение.  Анна Жукова. Фото: Софья Удачина

Задача: сохранить старину

— В ходе реставрации мы по максимуму старались сохранить первоначальный вид здания, – рассказывает Анна. — Свечной завод был построен в 1880-х годах согласно всем требованиям церковных канонов. Вся команда, работавшая над реставрацией, была нацелена на то, чтобы их соблюсти, сохранить старину и дух истории. Очень много сил ушло на восстановление кирпичной кладки, в ходе которого открылись спрятанные за штукатуркой архитектурные элементы.

Анна Жукова признается: во время ремонтных работ рука не поднималась просто взять и выбросить плитку с пола, поэтому та столетняя плитка, которая сохранилась, легла в основу нового ремонта. В одном из помещений, раньше служившем цехом, сама напольная плитка сохранилась плохо, но когда ее начали снимать, обнаружили отпечатавшиеся на цементе оттиски клейма «ХарьковЪ. 1887». Было решено накрыть часть пола стеклом и подсветить эту необычную деталь.

Так все находки становились частью сложной, но невероятно удачной дизайнерской композиции.

В стиле полтергейст

Не обошлось и без происшествий в стиле полтергейст. При реставрации лестницы в здании пару дней сильно пахло ладаном. И если этот момент еще как-то поддавался рациональному объяснению с точки зрения логики, – скажем, строители откололи штукатурку в месте, где во время бытности здания свечным заводом пролили миро, душистое церковное масло, – то один случай остается непонятным до сих пор. Когда строители укладывали кафель в помещении на втором этаже, то плитки стали нагреваться. Уровень нагрева был немаленький – после протирания влажной тряпкой от них шел пар. Это длилось несколько часов подряд. А к утру плитки остыли и больше пока не нагревались… 

Посетить выставку «Цветущая поляна Нины Луговской» можно ежедневно с 10.00 до 18.00. Справки по телефону: 8-910-777-49-49.

С приставкой «арт»

— Изначально музыкальная фабрика делилась на три корпуса: рояльный, струнный и административный, – говорит Анна Жукова. — Условно эти названия мы сохранили. Сейчас в холле рояльного корпуса (центральный вход в творческое пространство «Фабрика» – прим. корр.) находится пресс для музыкальных инструментов. Характерно, что станок до сих пор в рабочем состоянии. А своим присутствием здесь он обязан сложности демонтажа: чтобы его вывезти, нужно было разбирать перекрытия второго этажа, поэтому было принято простое и гениальное решение – оставить его в качестве арт-объекта.  Пресс для музыкальных инструментов. Фото: Софья Удачина

Вообще, надо сказать, что благодаря тонкому вкусу и чуткому руководству Анны Жуковой на «Фабрике» много всего с приставкой «арт». Причем не только в отделке, но и в наполнении. Так, например, на сегодняшний момент в коридорах первого этажа организована выставка картин художницы Нины Луговской, в одном из помещений расположился единственный в своем роде частный Музей русской иконы, а площади второго этажа арендуют престижные фотостудии и творческие мастерские.  В Музее русской иконы. Фото: Софья Удачина

— Мне хочется, чтобы это здание наполнилось креативом в самом лучшем смысле этого слова, – признается Анна. — Чтобы здесь работали и отдыхали, общались и вдохновлялись творческие владимирцы, чтобы стены творческого пространства «Фабрика» стали синонимом высокого художественного вкуса и частью культурной палитры города.

Опубликован в газете «Московский комсомолец» №12 от 13 марта 2019

Заголовок в газете: Старая фабрика на новый лад

вспоминаем самую противоречивую фигуру владимирской епархии

5 июня во время вечерни на Вселенскую Родительскую субботу на 94-м году жизни скончался духовник Свято-Боголюбского женского монастыря схиархимандрит Петр (Кучер). Во Владимирской области его имя знают не только верующие, но и многие миряне. Рассказываем, каким он был.

Сначала раскулачивание, потом оккупация

Петр Кучер родился 11 июля 1926 года в селе Клиновое Донецкой области в крестьянской семье. Детство, как почти у всех в то время, было тяжелое. В начале 1930-х семью раскулачили. В 1932-1933 годах, когда юг переживал голод, умер младший брат, в 1938 году умер отец, мать осталась одна с четырьмя детьми – Георгий (был призван на фронт с первых дней войны, несколько раз ранен, но вернулся живым), Василий (скончался от ран 16 апреля 1944 года), Петр и сестра Мария.

В начале войны Донбасс был оккупирован. Немцы периодически устраивали облавы, угоняя молодежь в Германию. Летом Петр с братом Василием целыми днями пасли в степи скот, чтобы не попасться на глаза немцам. Но однажды , когда Петр был дома, нацисты ворвались к ним, и мать в последний момент успела спрятать его в кухонный стол-тумбу. Согнувшись в три погибели, он просидел там, пока немцы прочесывали дом. Сам Петр Кучер говорил, что его спасло только то, что он был тогда невысокий и тощий от постоянного недоедания, а немцам и в голову не могло прийти, что подросток может поместиться в таком шкафчике. Сразу после освобождения Донбасса в сентябре 1943 года 17-летним юношей Петр был призван в действующую армию.

Петр Кучер участвовал в боях на территории Югославии, Румынии, Венгрии и Австрии, в Белградской, Будапештской и Венской наступательных операциях советских войск. Был награжден орденами Отечественной войны II степени, Славы III степени, медалями «За отвагу», «За освобождение Белграда», «За взятие Будапешта», «За взятие Вены».

Один из эпизодов войны отец Петр вспоминал неоднократно – в октябре 1944 года недалеко от Белграда их взвод попал в засаду – немцы расстреляли колонну советских войск почти в упор. Петр остался у орудия вдвоем с командиром взвода. Полтора часа они отбивались от немцев, прикрывая раненых, пока не подошли остальные подразделения полка. За этот бой он был награжден Орденом Славы III степени.

Никто не хотел пропустить в священники

После демобилизации Петр Кучер окончил вечернюю школу рабочей молодежи, потом институт. Работал до 1973 года на Украине. Но уже с 1963 года стал регулярно ходить в церковь, хотя и до этого был верующим, и всю войну носил с собой в кармане листок с молитвой.

Петр Кучер. Фото предоставлено Владимирской епархией

Долгое время будущий отец Петр не мог найти епархию, где бы его приняли хотя бы в штат церковнослужителей. В то время началась новая волна гонений на Церковь, и чиновники не могли пропустить в священники человека с его биографией – фронтовик, боевые награды, высшее образование…

В поисках архиерея, который мог бы посвятить его во священники, он исколесил полстраны, но везде получал отказ. Наконец, Кучеру намекнули, что ему нужно ехать в Латвию, так как там давление на церковь со стороны государства было поменьше. Там его посвятили в сан сначала диакона, а потом и священника (1975 год). В 1979 году отец Петр был назначен на должность духовника и старшего священника Спасо-Преображенской Пустыни близ города Елгавы от Рижского Свято-Троицкого женского монастыря. Уже тогда про отца Петра стала ходить слава твердого блюстителя монашеских порядков.

В 1991 году его приглашают в Липецк для восстановления Задонского Свято-Тихоновского Спасо-Преображенского монастыря. Отец Петр нашел инвесторов, договорился с администрацией города Липецка и возсоздал на руинах монастыря величественный собор, колокольню, отстроил заново весь комплекс монастырских зданий. К 1996 году на территории монастыря уже жили и работали более 110 насельниц.

Однако тут у отца Петра возникли трения религиозного характера. По сути, его обвиняли в ереси. В итоге, ему пришлось покинуть Задонский Тихоновский монастырь. В ноябре 1996 года он и с ним 63 насельницы Задонской обители переехали в Суздаль к старообрядцам. В расколе священник был лишь месяц, в декабре написав архиепископу Владимирскому и Суздальскому Евлогию. Кучер просил принять его и сестер обратно в лоно РПЦ. В начале января в Успенском соборе города Владимира Петр с сестрами приносят покаяние и отправляются восстанавливать Свято-Боголюбский монастырь.

Свято-Боголюбский монастырь, который восстанавливал Петр Кучер

Отец Петр участвовал в возрождении множества других храмов, куда он посылал помощь по просьбе верующих и духовенства, а в середине 2000-х годов вместе с ныне покойной игуменией Софией (Комаровой) из Суздальского Покровского монастыря построил новую церковь на 500 человек в Нижегородской епархии.

В течение более десяти лет – с 1998 по 2010 года по благословению отца Петра при монастыре воспитывались дети-сироты и дети из неблагополучных семей, однако после скандала 2010 года, когда несколько сбежавших девочек пожаловались на жестокое обращение в приюте, его пришлось закрыть.

После перенесенного в сентябре 2019 года инсульта отец Петр не имел возможности совершать богослужения, но молился у себя в келье, а, когда позволяли силы, и в алтаре. 8 мая 2020 года митрополит Владимирский и Суздальский Тихон совершил чин пострижения архимандрита Петра в великую схиму в честь святого первоверховного апостола Петра.

90000 Ask Me Anything session with a Kaggle Grandmaster Vladimir I. Iglovikov | by Vladimir Iglovikov 90001 90002 Hello, my name is Vladimir. 90003 90002 After graduating from university with a degree in theoretical physics, I moved to Silicon Valley in search of a data science role in the industry. This led me to my current position in Lyft’s autonomous vehicle division where I work on computer vision related applications. 90003 90002 In the past few years, I have invested a lot of time in Machine Learning competitions.On the one hand, it is pretty fun, but on the other, it is a very efficient way to boost some of your data science skills. I would not say that all the competitions came easy, and I would not say that I got good results in all of them. But from time to time I was able to get close to the top, which finally led to the title of Kaggle Grandmaster. 90003 90002 I am thankful to @Lasteg who proposed the idea for this AMA (Ask Me Anything session) and collected questions on Reddit, Kaggle, and science.d3.ru (In Russian). There are a lot of questions. I will try to answer what I can, but I will not be able to address all of them in this blog post. If there is a question that you asked but is not addressed in the following text, just write it in the comments and I will try to answer. 90003 90002 Here is the list of Deep Learning challenges in which I (or my team) was fortunate enough to place near the top of the leaderboard .: 90003 90002 90013 Q: Do you have a life outside of data? 90014 90003 90002 Yes, I do 🙂 90003 90002 I like backpacking and rock climbing.If you are climbing in the Mission Cliffs climbing gym in the mornings, you can say hi, next time you see me. 90003 90002 I also love partner dancing, Blues Fusion, in particular. Mission Fusion in San Francisco and South Bay Fusion dance venues is where I typically go. 90003 90002 Travel is also important to me. This spring I was in Belarus, Morocco, and Jordan. In September I spent three weeks in Finland, Germany, and Austria. And of course, Burning Man 2018 was the best experience of the year 🙂 90003 90002 90013 Q: Height / weight? 90014 90003 90002 6 feet, 185 lbs 90003 90002 I guess this question may be somehow related to working out, so let me throw some of my results in powerlifting from the times when I was in a grad school 🙂 90003 90032 90033 Maximum benchpress 90013 225 90014 lb 90036 90033 Maximum squat 90013 315 90014 lb 90036 90033 Maximum deadlift 90013 405 90014 lb 90036 90045 90002 90013 Q: How can you have a career and do kaggle full time? 90014 90003 90002 Working on Kaggle competitions is a second unpaid full-time job.You should have a pretty good reason to do this. It is common for active Kaggle participants to be seeking a change of field. I was not an exception. I started working on competitions when I was transitioning from academia to industry. I needed an efficient way to get used to problems that ML may solve, master the tools, and expand my way of thinking to the new world of machine learning. 90003 90002 Later, after I got my first job at Bidgely, I got involved in Kaggle even more. During the day I was working on signal processing tasks and almost all evenings were burnt on competitions with tabular data.My work-life balance was not great, but the amount of knowledge per unit of time that I was getting was worth it. 90003 90002 At some point, I was ready, changed jobs, and joined TrueAccord for a position where I was doing a lot of traditional ML. But it would be unwise to stop my Kaggle work. So it was traditional machine learning during the day and deep learning in the evenings and weekends. Work-life balance was even worse, but I was learning a lot and as a nice addition to the acquired skills became Kaggle Master.All this effort paid off when I was able to get a job in Level5 at Lyft, which is heavy on the application of deep learning techniques to a problem of autonomous driving. 90003 90002 Finally, I am not working on Kaggle full time. But I am still actively learning. There are plenty of exciting computer vision problems at work and I am trying to get more knowledge in the areas that Kaggle does not cover. I still make submissions to various competitions from time to time, but this is mainly to have a better understanding of the problem and challenges that participants are facing, which in turn helps to get the most from the information that is shared at the forum.90003 90002 90013 Q: What’re your day-to-day routines which help you be productive? How do you structure your day? 90014 90003 90002 First of all, I am not sure that I am very productive. 🙂 I am always looking for new ways to optimize my routines. 90003 90002 There are always more problems that one needs to solve and activities that one wants to do. Not all of them are equally useful and enjoyable. This means that I always need to put priorities on every action. There are a couple of books that have excellent discussions on the topic.I would recommend everyone who is thinking about becoming more productive to read them: 90003 90002 90013 So Good They Can not Ignore You: Why Skills Trump Passion in the Quest for Work You Love 90014 90013 90014 and 90013 Deep Work: Rules for Focused Success in a Distracted World. 90014 90003 90002 During the weekday, I wake up at 6 am and go to the rock climbing gym. This helps me to stay in shape and wakes me up for the upcoming day. After this, I drive to work. Our autonomous driving engineering center is located in Palo Alto, which is a bit sad for me.I prefer to live in the city. Driving is fun, but commuting is not. To spend the time of the commute more productively, I listen to audiobooks in the car. I would not say that I can be very focused on the book while commuting, but there is plenty of literature, mostly soft skill or business oriented that pair pretty well while driving. 90003 90002 I would like to say that I have a great work-life balance, but it would not be true. For sure, a decent amount of time is spent with friends, and at different events.Luckily something is happening all the time in San Francisco. At the same time, I still need to learn; I still need to stay in shape regarding machine learning. Not just pertaining to my problems at the office but on a much broader range. This means that some time in the evenings is spent on reading technical literature and writing code for competitions, side projects, or open-source projects. 90003 90002 Speaking of open source projects, I will take the opportunity to promote a library for image augmentations that Alexander Buslaev, Alex Parinov, Eugene Khvedchenia, and I created based on the insights we have acquired from our work on computer vision challenges.90003 90002 I suppose I can not finish this question without providing specific tricks 🙂 90003 90032 90033 I prefer Ubuntu + i3 to MacBook, subjectively + 10% to my performance. 90036 90033 My use of Jupyter notebooks is minimal. Only for EDA and visualizations. Almost all code that I write, I write in PyCharm, check with flake8, and commit to GitHub. Many ML problems are very similar. Investing in a better code base, trying not to repeat yourself, and thinking how best to refactor may slow progress in the beginning, but speeds you up later on.90036 90033 I write unit tests wherever possible. Everyone talks about the importance of unit tests in Data Science, but not everyone invests time in writing them. Alex Parinov wrote a nice doc that walks you on how to go from simple to complex. You may try to follow it and add more tests to your Academia or Kaggle ML pipelines. I am assuming that you are already doing it at work. 90036 90033 At the moment I am experimenting with model versioning tool DVC, which I hope will make results that my ML pipelines generate even more reproducible and code more reusable.90036 90033 I try to minimize the use of a mouse. Sometimes it means that I need to write hotkeys on paper, put it in front of me and try to use them as much as possible. 90036 90033 I do not use social networks. 90036 90033 I check email only a few times a day. 90036 90033 Every morning I create a list of tasks that I will try to achieve today and try to work on closing these tasks. I use Trello for this. 90036 90033 I am trying not to fragment my day too much. Many tasks require concentration and switching focus all the time is not helpful.90036 90045 90002 All these ideas are pretty standard, but I can not remember any magic trick in my pocket. 🙂 90003 90002 90013 Q: How do you keep up with current research in the field? 90014 90003 90002 I would not say that I do. The ML area is so dynamic these days, the number of papers, competitions, blog posts, and books is so huge that even skimming through them is impossible. In practice, when I am facing some problem, I focus on looking through the recent results and dig deep into it.After I am done with this problem, I switch to the next one. As a result, I only have high-level knowledge of areas that I do not have hands-on experience in, and I am ok with it. At the same time, the list of the problems that I have worked on, and consequently have deep expertise in, is relatively large and this list continues to grow. This fact reassures me that combinations of the experience and code that I have already written and keep in private repositories will help me to have a quick start in any new ML related task.90003 90002 And, besides, it means that for many problems I have pretty strong pipelines that are already implemented, which gives me a warm start when I will face a similar problem next time. 90003 90002 I also attend conferences like NIPS, CVPR, and others. Results presented there serve as a good proxy of what we can and what we can not in terms at the current stage of research. 90003 90002 90013 Q: Back in times (like 4-5 years ago) having a Ph.D. in the non-ML specific field (Physics, MechEng, etc.) Was very beneficial for the employers. Currently, I feel like the situation changed, and if one compares PhDs from the non-ML field vs. MSc from ML, it looks like the IT / ML industry will prefer the latter one for ML engineering / developers role, but I am not sure about research roles. Since you also have a Ph.D. in Physics and then transferred to ML, I think you might know the answer to the question from your current experience. 90014 90003 90002 90013 What is your opinion about having a non-ML Ph.D. now, if one wants to transfer to the ML industry afterward? Will it help to get a research position in a company? Will it help at all to find a job in the ML industry in comparison to relevant MSc? 90014 90003 90002 This is a hard question, I do not know the answer, so I will just think aloud. 90003 90002 Physics is a great major. Even if I could go back and choose between physics and CS, knowing that eventually, I would move to CS, I would still select physics. 90003 90002 The main reason, of course, is that I am excited about physics and the natural sciences in general.Does ML teach you how this big, colorful, exciting universe around us is working? Not really. But physics does. And it is not just this. One of the reasons why switching from physics to ML was relatively smooth is because physics, as a major, gave me not only knowledge of quantum mechanics, the theory of relativity, quantum field theory, and other highly specialized topics, but also gave me crucial skills in math, statistics, coding, all of which are easily transferable to other domains. 90003 90002 Physics teaches you how to maneuver between rigorous theory and experimental in a structured way; an essential skill for any ML practitioner.And it is close to impossible to learn physics or advanced math without universities, from self-education. On that note, it is my firm belief that the next strong breakthrough in deep learning will happen when we figure out how to apply advanced math, developed for physics, chemistry, and other advanced fields, to machine learning. Right now it is enough to know math at the level of the freshmen year in college to solve problems in computer vision. 90003 90002 All this means that at the moment math is not a blocker, and that is why additional knowledge that one gets in math / physics / chemistry and other STEM departments is close to useless for solving the most business problems, and that is why many graduates from these departments feel betrayed.They have a lot of specialized knowledge; they have Ph.D., they spent many years in academia. They are not able to get an interesting well-paid job. The internet is full of these types of blog posts. 90003 90002 Being able to write code, on the other hand, is essential everywhere and that is why when a potential employer chooses between a person who knows math well and one who writes code well, the second will almost always win. 90003 90002 But I believe it will change. Not now, but at some point in the future.90003 90002 It is important to note that papers that you read and the classes that you take in the university may not be directly relevant to the skillset that you will need in the industry. This is true, but I do not think that it is a big deal. 90003 90002 Typically, things that you need to know to work in the industry as a Data Scientist or Software Developer, you can learn by yourself, or you will not be able to learn in a university anyway. Most of the things that people learned in the industry can be acquired only at your full-time job at some company.90003 90002 At the same time, it was stressful for me to try to find a job in industry while working on my thesis in Theoretical Physics while studying data science. 90003 90002 I did not have all the required knowledge; I was not understanding how things in silicon valley are done and what is expected from me. I had close to zero network, and the only thing that I tried to do is to send my resume to different companies, again and again, failing the interview after the interview, learning from every failure, and repeating it until it somehow worked.90003 90002 I remember how one time I was asked what I was doing for my thesis? I was doing Quantum Monte Carlo, and I told the interviewer this. After it, I tried to explain what it means and why do we need it. The interviewer looked at me and asked: «And how this technique can help us to increase the engagement of the customers?» 90003 90002 I would say the approach that looks the most promising for those that have a non-CS major is to attend DS related classes within the CS department. Learn DS / ML in your free time.Luckily, there are plenty of great resources for this. I would say that it is a good idea to find a professor in your department that is interested in applying ML to his or her problems. Applying for ML related internships in tech companies and getting an internship as a grad student is easier than getting a full-time job. 90003 90002 Getting a full-time job after an internship is pretty straightforward. For example, my friend Wenjian Hu who was also studying physics in our research group did precisely this, and he got a Research Scientist position in Facebook AI Research.90003 90002 In general, it would be unwise to overestimate the influence of your major, university, etc. for finding a job in the industry. When a company is hiring you, they are planning to pay you money for solving the problems that they face. Your degree and your major is just a proxy to estimate your abilities. For sure, passing the resume filtering stage by HRs without lines in the resume that they expect to see is hard, and your networking, which is essential for job search will be weaker, but, again, it is not a thing that should influence the decision for your major.90003 90002 I may sound naive, but one should pick major, not because he / she believes that it will lead to a good salary, but because you are passionate about it. 90003 90002 90013 Q: Where would you say the interesting problems in Data science / ML currently lie? I am around 50% complete with my Masters and am not sure where in the ML pipeline I want to work. I spoke with someone who asserted the two best areas are in Algo creation and scaling (as opposed to DS / ML applications which may be more library plug and chug).What are your thoughts on this? / Any recommendation in terms of career flexibility. 90014 90003 90002 I would say that interesting problems in DS / ML lie far from what is mainstream today. Mainstream problems are crowded. Applying ML to credit scoring, recommender systems, retail, and other disciplines where we figured out how to map data into money is boring. But if you apply DS / ML to unsolved problems in math, physics, biology, chemistry, history, archeology, geology, or any other field where people did not try to apply ML that much, you may find your next Purple Cow.90003 90002 Regarding career choices. Unlike biology or physics, skills that you learn in DS / ML is much easier to transfer from field to field. For sure, developing algorithms for trading in some bank or hedge fund is not the same as working on self-driving cars, but the difference is not that big, and you will be able to pick up necessary skills pretty fast as long as you are good in the fundamentals. 90003 90002 90013 Q: Is 30 years age a too late frontier to join the ML community with the background, not in Math / CS? Or it’s possible to catch the last train? If yes, what are the minimal requirements for that in your opinion? 90014 90003 90002 Of course, it is not too late.90% of ML requires math knowledge at the level of the freshman year in a technical university, so no ultradeep math knowledge is needed. And the most widely used languages ​​in DS are a python and R, which are high level, so you can start using them without dying in technical details. 90003 90002 I would recommend to take a few online classes in DS and start working on problems on Kaggle. Of course, many concepts will sound new, but you just need to have some discipline and dedication, and everything will come.90003 90002 Two more examples related to age: 90003 90032 90033 Kaggle Grandmaster Evgeny Patekha started his journey to Data Science at the age of forty. 90036 90033 Kaggle Grandmaster Alexander Larko joined Kaggle at the age of fifty-five. 90036 90045 90002 Q: 90013 Do you consider formal fundamental education in the technical field essential to success in Data Science and Kaggle competitions? Do you have any contrary examples in your experience at work? 90014 90003 90002 Helpful yes.Essential no. There are plenty of people at Kaggle with great results that do not have fundamental education in the technical field. The stereotypical example is Mikel Bober-Irizar, who is Kaggle Grandmaster, but who still studies in a high school. 90003 90002 Another thing is that you need to remember that skills that you learn at Kaggle are only a small subset of the skills that you need when you work on ML in industry or academia. And for those skills that Kaggle does not develop fundamental education in the technical field may be crucial.90003 90002 But again. You can be good at Kaggle without a high school diploma. 90003 90002 90013 Q: How long did you learn Data Science / ML to get to a level where you were competitive in Kaggle? 90014 90003 90002 I decided to switch to data science in January 2015. After this, I started taking online classes at Coursera. At the end of February, I learned about Kaggle and registered there. In two months I got my first silver medal. 90003 90002 90013 Q: Can one achieve high kaggle results with a simple home computer and without a cloud? 90014 90003 90002 I do not use the cloud in competitions.But I have two relatively powerful computers at home. One with four GPUs, another with two. You can get good results at kaggle without a very powerful machine, but lack of the computing power will limit you regarding how many ideas you can check per unit of time. And the number of these ideas that you check is strongly correlated with your result. So, if you are training models 24/7, you should probably invest in a good desktop. 90003 90002 After a few iterations, I ended with the following dev box with four GPUs for heavy lifting and a desktop with two GPUs for prototyping.90003 90002 At the same time having a powerful machine is not enough. You need to be able to write code that can take advantage of it. 90003 90032 90033 One of the reasons why I switched from Keras to Pytorch is because DataLoader in PyTorch was far more superior at the time. 90036 90033 We wrote 90208 albumentations 90209 because imgaug was too slow and we got 100% CPU utilization, while GPUs were not fully utilized. 90036 90033 To speed up jpeg image I / O from the disk one should not use 90208 PIL 90209, 90208 skimage, 90209 and even 90208 OpenCV 90209 but look for 90208 libjpeg-turbo 90209 or 90208 PyVips 90209.90036 90045 90002 etc. 90003 90002 90013 Q: Is there any advice he can make on where to start with Kaggle to freshly starting data scientist aspirants? Best advice for a competition noob joining their first competition? 90014 90003 90002 There are many ways to get into Kaggle, but based on what I observed one of the most efficient ways to get the required knowledge is to use the hacker’s approach. 90003 90232 90033 Watch some online classes that cover the basics of python programming and machine learning.90036 90033 Pick a competition at Kaggle. If you can write an end2end pipeline that maps data to the submission, this is great. If you are new, it may be hard. In this case, go to the forum and copy-paste kernel that someone shared. 90036 90033 Run it on your computer, generate a submission, and appear on the leaderboard. At this step, most likely you will feel the pain of the operating system, drivers, library versions, I / O issues, etc. It is important to start getting used to it as early as possible.If you have no clue what was happening in that kernel, it is fine. 90036 90033 Tweak a few parameters, doing it blindly is ok, retrain your models, submit your predictions. Hopefully, some of your modifications will move you up at the leaderboard. And do not worry, hundreds of people around you are doing the same. They are adjusting different knobs that they can reach without in-depth knowledge or intuition why something is happening. 90036 90033 To get above all those people that are blindly tweaking parameters you need to start developing intuition and gain fundamental knowledge on what may and what may not work so that you can explore the phase space of possible approaches more intelligently and efficiently.At this step, you will need to add studying to your experimentation. You will need to learn in two directions. First — fundamentals, classes like mlcourse.ai, CS231n, reading books, studying math, statistics, how to write better code, etc. Typically, it is hard to make yourself do it, but in the long-term perspective, it is critical. Second, you will see plenty of new terms at the forum related to the problem that you are trying to solve. Focus on them. Try to use your urge to get better at the leaderboard as an extra motivation to learn new things.But do not choose between studying and experimentation with your pipeline — do both at the same time. Machine learning is an applied discipline, and you do not want your knowledge to become purely bookish. Theory without practice is stupid. Practice without theory is blind. 90036 90033 After the competition is over, despite all your efforts, most likely, you will see yourself pretty low at the leaderboard. This is expected. Read carefully through the forum, read solutions that winners shared, try to understand what could you do better.Next time when you will see a similar problem, your starting point will be much higher. 90036 90033 Repeat the process on many competitions, and you will get to the top. What is more important is that you will have good pipelines for many problems at your disposal, and well-developed intuition on how to deal with machine learning challenges that you face at competitions, at work, or in academia. 90036 90247 90002 90013 Q: As someone from a physics background, does it sometimes frustrate you when competitions are more of an overfitting exercise vs.actual generalization at a specific task? Assuming so, how do you cope? 90014 90003 90002 Typically, you need to overfit the data and metrics to get a good result. This is fine, and this is expected. People overfit on the ImageNet dataset for so many years, and new knowledge is still generated in the process. But to do it you need to understand nuances of the metric and data. And this is where knowledge comes. As long as new knowledge is created during the time of the challenge I am fine with overfitting.As you may notice, pipelines, and ideas that were good at one problem serve as a reliable baseline on the next one, which speaks of some generalizability. 90003 90002 90013 Q: What are your thoughts on the data leaks at Kaggle, for instance, Santander, Airship prediction, and Google Analytics? Is it ethical to use data leakage in Kaggle competitions? 90014 90003 90002 I acknowledge that organizing competition is very hard, so I do not blame organizers when the leak is discovered. I am also fine when people take advantage of the leaks.I need to admit that leaks discourage me from participating in a challenge, but it is mainly to the fact that I would not be able to generalize acquired knowledge to the other challenged that easily. I still believe that Kaggle admins need to create a checklist of possible data leaks and check the data before the challenge to prevent the same issues happening again and again, but I believe they are working on this. 90003 90002 90013 Q: How useful is Kaggle competitions wrt business / working as A DL Eng.? 90014 90003 90002 It is tough to say. Kaggle boosts your skillset in a few critical but very narrow fields. It is an important skill set, and for some positions, it may be highly beneficial, while for others it will not. For all jobs that I worked, and especially now when I work on self-driving, Kaggle skills are a powerful addition to a set of skills that I got from academia and other sources of knowledge. 90003 90002 But again, having Kaggle skills, even if they are solid is not enough. Many things you can learn only in the industry.90003 90002 Being a Kaggle Master is not necessary and not enough to be good at what you do at work, but at the same time, I believe that if a person is a Kaggle Master, it should be enough to pass resume filtering stages by HR and invite a person to a tech screen. 90003 90002 90013 Q: How useful is taking part in Kaggle competitions after becoming Grandmaster? What is your motivation to do Kaggle when you are already an accomplished data scientist? 90014 90003 90002 As I mentioned, I do not participate in Kaggle competitions that much anymore, but I started looking at competitions that are provided in conjunction with different conferences.My team had a good result at MICCAI 2017, CVPR 2018, and MICCAI 2018. Competitions typically include nice, clean data sets that require minimal data cleaning, and allows you to focus less on the data and more on the numerical techniques. This is a luxury that you typically do not have at work, where the data selection process is typically the most important component of creating a useful pipeline. 90003 90002 90013 Q: What would you recommend to undergraduates and graduates from your learning and competitive experiences? What milestones must one set to attain mastery of Data Science? 90014 90003 90002 I do not even know what mastery in data science is.There are plenty of ways to answer this question. But in this AMA I am talking as a Kaggle Grandmaster, so let’s say your first milestone should be to become Kaggle Master. It is relatively straightforward, but while you are working on it, you will have a better vision of what you want in this domain. 90003 90002 90013 Q: How far can you get on Kaggle (and in the Data Science field more broadly) without an educational background in Mathematics / Computer Science or some other highly numerate subject? How far can passion and a desire to learn instead take you? 90014 90003 90002 You can get to the top of the top in Kaggle or any other data science field if you are goal-oriented and are willing to learn.The hardest step is the first step. Just do it. And the 90003.90000 Vladimir Lenin | Biography, Facts, & Ideology 90001 90002 The making of a revolutionary 90003 90004 It is difficult to identify any particular events in his childhood that might prefigure his turn onto the path of a professional revolutionary. Vladimir Ilich Ulyanov was born in Simbirsk, which was renamed Ulyanovsk in his honour. (He adopted the pseudonym Lenin in тисячі дев’ятсот одна during his clandestine party work after exile in Siberia.) He was the third of six children born into a close-knit, happy family of highly educated and cultured parents.His mother was the daughter of a physician, while his father, though the son of a serf, became a schoolteacher and rose to the position of inspector of schools. Lenin, intellectually gifted, physically strong, and reared in a warm, loving home, early displayed a voracious passion for learning. He graduated from high school ranking first in his class. He distinguished himself in Latin and Greek and seemed destined for the life of a classical scholar. When he was 16, nothing in Lenin indicated a future rebel, still less a professional revolutionary-except, perhaps, his turn to atheism.But, despite the comfortable circumstances of their upbringing, all five of the Ulyanov children who reached maturity joined the revolutionary movement. This was not an uncommon phenomenon in tsarist Russia, where even the highly educated and cultured intelligentsia were denied elementary civil and political rights. 90005 90006 Little Octobrist badge 90007 Little Octobrist badge with a portrait of a young Vladimir Ilich Lenin. 90008 Gertha 90009 90004 As an adolescent Lenin suffered two blows that unquestionably influenced his subsequent decision to take the path of revolution.First, his father was threatened shortly before his untimely death with premature retirement by a reactionary government that had grown fearful of the spread of public education. Second, in тисячі вісімсот вісімдесят сім his beloved eldest brother, Aleksandr, a student at the University of St. Petersburg (later renamed Leningrad State University), was hanged for conspiring with a revolutionary terrorist group that plotted to assassinate Emperor Alexander III. Suddenly, at age 17, Lenin became the male head of the family, which was now stigmatized as having reared a «state criminal.»90005 Get exclusive access to content from our тисяча сімсот шістьдесят вісім First Edition with your subscription. Subscribe today 90006 Top Questions: Vladimir Lenin 90007 Questions and answers about Russian revolutionary leader Vladimir Lenin. 90008 Encyclopædia Britannica, Inc. 90009 See all videos for this article 90004 Fortunately the income from his mother’s pension and inheritance kept the family in comfortable circumstances, although it could not prevent the frequent imprisonment or exile of her children. Moreover, Lenin’s high school principal (the father of Aleksandr Kerensky, who was later to lead the Provisional government deposed by Lenin’s Bolsheviks in November [October, O.S.] 1917) did not turn his back on the «criminal’s» family. He courageously wrote a character reference that smoothed Lenin’s admission to a university. 90005 90004 In autumn 1887 Lenin enrolled in the faculty of law of the imperial Kazan University (later renamed Kazan [VI Lenin] State University), but within three months he was expelled from the school, having been accused of participating in an illegal student assembly . He was arrested and banished from Kazan to his grandfather’s estate in the village of Kokushkino, where his older sister Anna had already been ordered by the police to reside.In the autumn of 1888 the authorities permitted him to return to Kazan but denied him readmission to the university. During this period of enforced idleness, he met exiled revolutionaries of the older generation and avidly read revolutionary political literature, especially Marx’s 90019 Das Kapital 90020. He became a Marxist in January 1889. 90005.