#kaggle

[d0nutzgg]

Tonight I am hunting down venomous and nonvenomous snake pictures that are under the creative commons of specific breeds in order to create one of the most advanced, in depth datasets of different venomous and nonvenomous snakes as well as a test set that will include snakes from both sides of all species. I love snakes a lot and really, all reptiles. It is definitely tedious work, as I have to make sure each picture is cleared before I can use it (ethically), but I am making a lot of progress! I have species such as the King Cobra, Inland Taipan, and Eyelash Pit Viper among just a few! Wikimedia Commons has been a huge help!

I'm super excited.

Hope your nights are going good. I am still not feeling good but jamming + virtual snake hunting is keeping me busy!

[techniktagebuch]

April 2023

Sechs Jahre Nichtstun, eine schöne Lösung für so viele Probleme

Vor fast genau sechs Jahren habe ich beschlossen, auch mal dieses Machine Learning auszuprobieren:

Gleich kann es losgehen, ich muss nur erst “Getting Started before your first lesson” lesen. Von dort schickt man mich weiter zum AWS deep learning setup video. Das Video ist 13 Minuten lang.

(Es folgen Probleme und Verwicklungen beim Setup, die Details kann man hier nachlesen.)

In Minute 12:45 sagt der Erzähler im Video: “Ok! It looks like everything is set up correctly and you’re ready to start using it.” Aber statt 12 Minuten und 45 Sekunden sind zwei Wochen vergangen, mein anfänglicher Enthusiasmus ist aufgebraucht und mein Interesse an Deep Learning erlahmt. Ich bin nicht einmal bis “Lesson 1” gekommen.

Im April 2023 sagt Aleks, dass er gerade einen sehr guten Onlinekurs über Machine Learning macht. Ich frage nach der Adresse, und sie kommt mir bekannt vor. Es ist derselbe Kurs!

“Das Setup war kein Problem?”, frage ich. Nein, sagt Aleks, Sache von ein paar Minuten.

Ich sehe mir "Practical Deep Learning for Coders 2022” an. Man braucht für den Kurs bestimmte Hardware. Generell benötigt Machine Learning Grafikprozessoren wegen der höheren Rechenleistung, und aus der Einleitung zum Kurs weiß ich jetzt, dass die aktuell verfügbaren Tools Nvidia-Grafikprozessoren voraussetzen*. Den Zugang zu dieser Hardware soll man mieten. Das war vor sechs Jahren auch schon so, nur dass das Mieten der Rechenleistung bei Amazon Web Services eine komplizierte und teure Sache war.

* Ich hatte an dieser Stelle schon “Grafikkarten” geschrieben, dann kam es mir aber wieder so vor, als müsste ich meinen Sprachgebrauch renovieren. In meiner Vorstellung handelt es sich um eine Steckkarte, ungefähr 10 x 20 cm groß, die in ein PC-Gehäuse eingebaut wird. So war das, als ich meine Computer noch in Einzelteilen kaufte, aber das ist zwanzig Jahre her. Deshalb habe ich mich für das unverbindliche Wort “Grafikprozessoren” entschieden. Aber wenn ich nach nvidia gpu machine learning suche, sehe ich sperrige Dinge, die nicht weit von meiner Erinnerung an Grafikkarten entfernt sind. Die große Rechenleistung braucht auch große Kühlleistung, deshalb sind zwei Lüfter auf der ... naja, Karte. Die Ergebnisse der Bildersuche sind etwas uneindeutig, aber es kommt mir so vor, als enthielte das Rechenzentrum, dessen Leistung ich gleich nutzen werde, wahrscheinlich große Gehäuse, in denen große Grafikkarten drin sind, vom Format her immer noch ungefähr wie vor zwanzig Jahren. Nur viel schneller.

2018 brauchte man AWS schon nicht mehr für den fast.ai-Onlinekurs. Stattdessen konnte man sich die Arbeitsumgebung bei Paperspace einrichten, einem anderen Cloud-Anbieter. Die Anleitung von 2018 klingt so, als hätte meine Geduld wahrscheinlich auch dafür nicht gereicht.

In der Version von 2019 hat der Kurs auf Google Colab gesetzt. Das heißt, dass man Jupyter Notebooks auf Google-Servern laufen lassen kann und keine eigene Python-Installation braucht, nur einen Browser. Colab gab es 2017 noch nicht, es wurde erst ein paar Monate nach meinem Scheitern, im Herbst 2017, für die Öffentlichkeit freigegeben. Allerdings klingt die Anleitung von 2019 immer noch kompliziert.

2020 wirkt es schon schaffbarer.

Auch die aktuelle Version des Kurses basiert auf Colab. Man muss sich dafür einen Account bei Kaggle einrichten. Soweit ich es bisher verstehe, dient dieser Kaggle-Zugang dazu, die Sache kostenlos zu machen. Colab würde ansonsten Geld kosten, weniger als ich 2017 bezahlt habe, aber eben Geld. Oder vielleicht liegen auch die Jupyter Notebooks mit den Kurs-Übungen bei Kaggle, keine Ahnung, man braucht es eben. (Update: In Kapitel 2 des Kurses merke ich, dass es noch mal anders ist, man hätte sich zwischen Colab und Kaggle entscheiden können. Zusammengefasst: Ich verstehe es nicht.)

Ich lege mir einen Kaggle-Account an und betrachte das erste Python-Notebook des Kurses. Es beginnt mit einem Test, der nur überprüft, ob man überhaupt Rechenleistung bei Kaggle in Anspruch nehmen darf. Das geht nämlich erst, wenn man eine Telefonnummer eingetragen und einen Verifikationscode eingetragen hat, der an diese Telefonnummer verschickt wird. Aber das Problem ist Teil des Kursablaufs und deshalb genau an der Stelle erklärt, an der es auftritt. Es kostet mich fünf Minuten, die vor allem im Warten auf die Zustellung der SMS mit dem Code bestehen.

Danach geht es immer noch nicht. Beim Versuch, die ersten Zeilen Code laufen zu lassen, bekomme ich eine Fehlermeldung, die mir sagt, dass ich das Internet einschalten soll:

“STOP: No internet. Click ‘>|’ in top right and set ‘Internet’ switch to on.”

Ich betrachte lange alles, was mit “top right” gemeint sein könnte, aber da ist kein solcher Schalter. Schließlich google ich die Fehlermeldung. Andere haben das Problem auch schon gehabt und gelöst. Der Schalter sieht weder so aus wie in der Fehlermeldung angedeutet, noch befindet er sich oben rechts. Man muss ein paar Menüs ein- und ein anderes ausklappen, dann wird er unten rechts sichtbar.

Ich bin also im Internet und muss erst das Internet einschalten, damit ich Dinge im Internet machen kann.

Aleks meint, wenn ich ihm gestern dabei zugehört hätte, wie er eine Viertelstunde lang laut fluchte, hätte ich schon gewusst, wie es geht. Hatte ich aber nicht.

Nach dem Einschalten des Internets kann ich das erste Jupyter-Notebook des Kurses betrachten und selbst ausprobieren, ob es wohl schwer ist, Frösche von Katzen zu unterscheiden. Für die Lösung aller Startprobleme von 2017 habe ich zwei Wochen gebraucht. 2023 noch eine Viertelstunde, und ich bin zuversichtlich, dass man um 2025 direkt in den Kurs einsteigen können wird.

(Kathrin Passig)

[jamalir]

5-Day Gen AI Intensive Course with Google Learn Guide | Kaggle

[anandshivam2411]

Python Basics and Data Analytics: Your First Step Towards Data Mastery

If you’re looking to dive into the world of data science, starting with Python programming and data analytics is a smart move. Python is one of the most popular programming languages, known for its simplicity and versatility, making it an excellent choice for beginners. This guide will help you understand the fundamental concepts and set you on the path to mastering data analytics.

Why Python? Python is favored by many data scientists due to its readability and extensive libraries like Pandas, NumPy, and Matplotlib. These libraries simplify data manipulation, analysis, and visualization, allowing you to focus on drawing insights rather than getting lost in complex code.

Key Concepts to Learn: Start by familiarizing yourself with Python basics, such as variables, data types, loops, and functions. Once you have a good grasp of the fundamentals, you can move on to data analysis concepts like data cleaning, manipulation, and visualization.

Hands-On Practice: The best way to learn is by doing. Engage in projects that allow you to apply your skills, such as analyzing datasets or creating visualizations. Websites like Kaggle provide real-world datasets for practice.

Resources for Learning: Numerous online platforms offer courses and tutorials tailored for beginners. Consider checking out resources on lejhro bootcamp, or even free tutorials on YouTube.

Free Masterclass Opportunity: To enhance your learning experience, there’s a free masterclass available that covers the essentials of Python programming and data analytics. This is a fantastic chance to deepen your understanding and gain valuable insights from experts. Be sure to visit this Python Programming and Data Analytics Fundamentals to secure your spot!

Embarking on your journey into Python programming and data analytics is an exciting opportunity. By building a solid foundation, you’ll be well-equipped to tackle more complex data challenges in the future. So, start your journey today and take that first step towards data mastery!

[edcater1]

Best way to learn data analysis with python

The best way to learn data analysis with Python is to start with the basics and gradually build up your skills through practice and projects. Begin by learning the fundamentals of Python programming, which you can do through online courses, tutorials, or books. Once you are comfortable with the basics, focus on learning key data analysis libraries such as Pandas for data manipulation, NumPy for numerical operations, and Matplotlib or Seaborn for data visualization.

After you grasp the basics, apply your knowledge by working on real datasets. Platforms like Kaggle offer numerous datasets and competitions that can help you practice and improve your skills. Additionally, taking specialized data analysis courses online can provide structured learning and deeper insights. Consistently practicing, participating in communities like Stack Overflow or Reddit for support, and staying updated with the latest tools and techniques will help you become proficient in data analysis with Python.

[govindhtech]

Benefits of Gemma on GKE for Generative AI

Gemma On GKE: New features to support open generative AI models

Now is a fantastic moment for businesses using AI to innovate. Their biggest and most powerful AI model, Gemini, was just released by Google. Gemma, a family of modern, lightweight open models derived from the same technology and research as the Gemini models, was then introduced. In comparison to other open models, the Gemma 2B and 7B models perform best-in-class for their size.

They are also pre-trained and come with versions that have been fine-tuned to facilitate research and development. With the release of Gemma and their expanded platform capabilities, they will take the next step towards opening up AI to developers on Google Cloud and making it more visible.

Let’s examine the improvements they introduced to Google Kubernetes Engine (GKE) now to assist you with serving and deploying Gemma on GKE Standard and Autopilot:

Integration with Vertex AI Model Garden, Hugging Face, and Kaggle: As a GKE client, you may begin using Gemma in Vertex AI Model Garden, Hugging Face, or Kaggle. This makes it simple to deploy models to the infrastructure of your choice from the repositories of your choice.

GKE notebook using Google Colab Enterprise: Developers may now deploy and serve Gemma using Google Colab Enterprise if they would rather work on their machine learning project in an IDE-style notebook environment.

A low-latency, dependable, and reasonably priced AI inference stack: They previously revealed JetStream, a large language model (LLM) inference stack on GKE that is very effective and AI-optimized. In addition to JetStream, they have created many AI-optimized inference stacks that are both affordable and performante, supporting Gemma across ML Frameworks (PyTorch, JAX) and powered by Cloud GPUs or Google’s custom-built Tensor Processor Units (TPU).

They released a performance deepdive of Gemma on Google Cloud AI-optimized infrastructure earlier now a days, which is intended for training and servicing workloads related to generative AI.

Now, you can utilise Gemma to create portable, customisable AI apps and deploy them on GKE, regardless of whether you are a developer creating generative AI applications, an ML engineer streamlining generative AI container workloads, or an infrastructure engineer operationalizing these container workloads.

Vertex AI Model Garden, hugging face, and connecting with Kaggle

Their aim is to simplify the process of deploying AI models on GKE, regardless of the source.

Putting a Face Hug

They established a strategic alliance with Hugging Face, one of the go-to places for the AI community, earlier this year to provide data scientists, ML engineers, and developers access to the newest models. With the introduction of the Gemma model card, Hugging Face made it possible for Gemma to be deployed straight to Google Cloud. You may choose to install and serve Gemma on Vertex AI or GKE after selecting the Google Cloud option, which will take you to Vertex Model Garden.

Model Garden Vertex

Gemma now has access to over 130 models in the Vertex AI Model Garden, including open-source models, task-specific models from Google and other sources, and enterprise-ready foundation model APIs.

Kaggle

Developers can browse through thousands of trained, deployment-ready machine learning models in one location with Kaggle. A variety of model versions (PyTorch, FLAX, Transformers, etc.) are available on the Gemma model card on Kaggle, facilitating an end-to-end process for downloading, installing, and managing Gemma on a GKE cluster. Customers of Kaggle may also choose to “Open in Vertex,” which directs them to Vertex Model Garden and gives them the option to deploy Gemma as previously mentioned on Vertex AI or GKE. Gemma’s model page on Kaggle allows you to examine real-world examples that the community has posted using Gemma.

Google Colab Enterprise

Notebooks from Google Colab Enterprise

Through Vertex AI Model Garden, developers, ML engineers, and ML practitioners may now use Google Colab Enterprise notebooks to deploy and serve Gemma on GKE. The pre-populated instructions in the code cells of Colab Enterprise notebooks provide developers, ML engineers, and scientists the freedom to install and perform inference on GKE using an interface of their choice.

Serve Gemma models on infrastructure with AI optimizations

Performance per dollar and cost of service are important considerations when doing inference at scale. With Google Cloud TPUs and GPUs, an AI-optimized infrastructure stack, and high-performance and economical inference, GKE is capable of handling a wide variety of AI workloads. 

By smoothly combining TPUs and GPUs, GKE enhances their ML pipelines, enabling us to take use of each device’s advantages for certain jobs while cutting down on latency and inference expenses. For example, they deploy a big text encoder on TPU to handle text prompts effectively in batches. Then, they use GPUs to run their proprietary diffusion model, which uses the word embeddings to produce beautiful visuals. Yoav HaCohen, Ph.D., Head of Lightricks’ Core Generative AI Research Team.

Gemma using TPUs on GKE

The most widely used LLMs are already supported by a number of AI-optimized inference and serving frameworks that now enable Gemma on Google Cloud TPUs, should you want to employ Google Cloud TPU accelerators with your GKE infrastructure. Among them are:

Jet Stream Today

They introduced JetStream(MaxText) and JetStream(PyTorch-XLA), a new inference engine particularly made for LLM inference, to optimise inference performance for PyTorch or JAX LLMs on Google Cloud TPUs. JetStream provides good throughput and latency for LLM inference on Google Cloud TPUs, marking a major improvement in both performance and cost effectiveness. JetStream combines sophisticated optimisation methods including continuous batching, int8 quantization for weights, activations, and KV caching to provide efficiency while optimising throughput and memory utilisation. Google’s suggested TPU inference stack is called JetStream.

Use this guide to get started with JetStream inference for Gemma on GKE and Google Cloud TPUs.

Gemma using GPUs on GKE

The most widely used LLMs are already supported by a number of AI-optimized inference and serving frameworks that now enable Gemma on Google Cloud GPUs, should you want to employ Google Cloud GPU accelerators with your GKE infrastructure.

What is vLLM

To improve serving speed for PyTorch generative AI users, vLLM is an open-source LLM serving system that has undergone extensive optimisation.

Some of the attributes of vLLM include:

An improved transformer programme using PagedAttention

Continuous batching to increase serving throughput overall

Tensor parallelism and distributed serving across several GPUs

To begin using vLLM for Gemma on GKE and Google Cloud GPUs, follow this tutorial

Text Generation Inference (TGI)

Text creation Inference (TGI), an open-source LLM serving technology developed by Hugging Face, is highly optimised to enable high-performance text generation during LLM installation and serving. Tensor parallelism, continuous batching, and distributed serving over several GPUs are among the features that TGI offers to improve overall serving performance.

Hugging Face Text Generation Inference for Gemma on GKE and Google Cloud GPUs may be used with the help of this tutorial.

Tensor RT-LLM

To improve the inference performance of the newest LLMs, customers utilising Google cloud GPU VMs with NVIDIA Tensor Core GPUs may make use of NVIDIA Tensor RT-LLM, a comprehensive library for compiling and optimising LLMs for inference. Tensor RT-LLM supports features like continuous in-flight batching and paged attention.

This guide will help you build up NVIDIA Tensor Core GPU-powered GPU virtual machines (GKE) and Google Cloud GPU VMs for NVIDIA Triton with Tensor RT LLM backend.

Google Cloud provides a selection of options to meet your needs, whether you’re a developer utilising Gemma to design next-generation AI models or choosing training and serving infrastructure for those models. GKE provides an independent, adaptable, cost-effective, and efficient platform for AI model development that may be used to the creation of subsequent models.

Read more on Govindhtech.com

[thenewsfactsnow]

Meet Gemma: Your New Secret Weapon for Smarter AI Solutions from Google!

Gemma, Google’s new generation of open models is now a reality. The tech major released two versions of a new lightweight open-source family of artificial intelligence (AI) models called Gemma on Wednesday, February 21. Gemma is a group of modern, easy-to-use models made with the same advanced research and technology as the Gemini models. Created by Google DeepMind and other teams at Google,…

[pulipuli]

看看網頁版全文 ⇨ 鐵達尼號生存者資料集 / Dataset: Titanic Survived https://blog.pulipuli.info/2023/07/dataset-titanic-survived.html 這份資料集改編自Kaggle所發佈的鐵達尼號生存者資料集。 可作為機器學習練習使用。 ---- # 資料來源 / Source https://www.kaggle.com/competitions/titanic/data。 # 資料集下載 / Download 這份資料集分成訓練集 Titanic-Survived.train.ods 與測試集 Titanic-Survived.test.ods。 ## 訓練集 / Train set - Google試算表線上檢視 - ODS格式下載 - OpenDoucment Spreadsheet (.ods) 格式備份：Google Drive、GitHub、One Drive、Mega、Box、MediaFire、pCloud、Degoo、4shared 訓練集將用於構建機器學習的模型，並具備每位乘客是否存活的結果。 機器學習模型應根據乘客的性別和艙位等「屬性」來建立，或是使用其他特徵工程(feature engineering)的技術來建造新的屬性。 ## 測試集 / Test set - Google試算表線上檢視 - ODS格式下載 - OpenDoucment Spreadsheet (.ods) 格式備份：Google Drive、GitHub、One Drive、Mega、Box、MediaFire、pCloud、Degoo、4shared 測試集則是用於評估模型的表現。 原本的測試集並不會告訴你每位乘客是否存活，僅是讓機器學習模型用來預測結果。 為了方便大家練習，我將測試集的結果加了上去。 # 案例數 / Instacnes - 訓練集：890 - 測試集：418 # 屬性 / Attributes 本資料集有部分屬性有所缺失，建立模型的時候需要特別處理。 # 目標屬性 / Target class。 「Survived」，也就是鐵達尼號的乘客是否生存。 ---- 文章最後要來問的是：你認為什麼屬性是影響乘客最後是否存活的關鍵呢？。 - 1. 船票等級：越高級表示越有錢，應該更容易存活吧？ - 2. 性別：男生身體力壯，應該更容易存活吧？ - 3. 年齡：青壯年應該比老人或小孩更容易存活吧？ - 4. 登船港口：愛爾蘭上來的乘客，說不定是海盜的後代，更容易存活？ - 5. 其他：是否還有其他因素與存活率有關？ 歡迎在下面說說你的看法喔！ ---- 看看網頁版全文 ⇨ 鐵達尼號生存者資料集 / Dataset: Titanic Survived https://blog.pulipuli.info/2023/07/dataset-titanic-survived.html

[dztechs]

دليل المبتدئين إلى Kaggle لعلوم البيانات

على الرغم من الزيادة الأخيرة في شعبيتها ، إلا أنَّ البيانات الضخمة لا تزال غير مُؤكدة نسبيًا مُقارنةً بمجالات التكنولوجيا الأخرى الراسخة. نتيجة لذلك ، يجد معظم المُبتدئين صعوبة في ممارسة ودراسة النظريات والمفاهيم بسبب نقص البيانات والموارد. ومع ذلك ، باستخدام Kaggle لعلوم البيانات ، يُمكنك التغلب على هذه المشكلة مع القليل من الإجهاد أو بدونه. إذن ، ما هي Kaggle ، وكيف يُمكنك أن تُصبح مطورًا محترفًا على هذه المنصة؟ هنا ، ستحصل على نظرة عامة على أداة علم البيانات المُتميزة هذه وستفهم سبب قضاء العديد من المُحترفين لساعات في استخدامها. استمر في القراءة لاكتشاف المزيد. تحق�� من أفضل الطرق السهلة للحصول على تجربة SQL قبل وظيفتك الأولى. Read the full article

[cockroachgirl1997]

if i manage to keep up the pace i have been studying at lately, i could be done with my data science course in february 🖥️⌨️

[39oa]

mini summer break update... new entries to the f1 rpf centrality graph (PIA, SAR) + oscar has had by far the largest relative increase in ship fic since i first pulled data back in april 👨‍🍳

f1 rpf graphing & archive insights

intro & prior work

hello! if you're reading this, you may already be familiar with my previous post about graphing hockey rpf ships and visualizing some overarching archive insights (feel free to check it out if you aren't, or alternatively just stick around for this intro). i've been meaning to make an f1 version of that post for a while, especially since i've already done a decent amount of f1 rpf analysis in the past (i have a very rough post i wrote a year ago that can be read here, though fair warning that it really does not make any sense; while i've redone a few viz from it for this post i just figured i'd link it solely because there are other things i didn't bother to recalculate!)

f1 is quite different from many team sports because a large part of my process for hockey was discovering which ships exist in the first place—when there are thousands and thousands of players who have encountered one another at different phases of their careers, it's interesting to see how people are connected and it's what was personally interesting to me about making my hockey graphs. however, with f1's relative pursuit of "exclusivity," barriers to feeder success and a slower-to-change, restrictive grid of 20 drivers, it becomes generally expected that everyone has already interacted with one another in some fashion, or at least exists at most 2 degrees of separation from another driver. because of this, i was less interested in "what relationships between a large set of characters exist?" (as per my hockey post) and more so in "what do the relationships between a small set of characters look like?"

process

my methodology for collecting "ship fic" tries to answer the question: what does shippability really look like on ao3? (the following explanation is adapted from my hockey post:) a perceived limitation i have with character tagging numbers on ao3 is that they don’t exactly reflect holistic ship fic; that is, if lando is tagged as a character in a max/daniel fic, it gets attributed to his character tag but doesn’t actually say anything about how many Relationship Fics exist for him on a whole. my best solution for this was essentially uncovering most of a driver's relationships and summing their individual fic counts to create an approximate # of “relationship fics” for each player. so any kind of shippability graph going forward will use that metric.

i used ao3’s relationship tag search and filtered by canonical in the formula 1 rpf fandom and only pulled relationship* fics (“/” instead of “&”) with a min. of 5 works. ao3’s counts are… Not the most accurate, so my filtering may have fudged some things around or missed a few pairings on the cusp, which again is why all the visuals here are not meant to show everything in the most exact manner but function more so as a “general overview” of ficdom. although i did doublecheck the ship counts so the numbers themselves are accurate as of time of collection.

(*i excluded wag ships, reader ships, threesomes to make my life easier—although i know this affects numbers for certain drivers, team principal/trainer/engineer ships, and any otherwise non-driver ship. i left in a few ships with f2, fe, etc. drivers given that that one character was/is an f1 driver, but non-f1 drivers were obviously excluded from any viz about f1 driver details specifically. this filtering affected some big ships like felipe massa/rob smedley, ot3 combinations of twitch quartet and so on, which i recognize may lower the… accuracy? reliability??? of certain graphs, but i guess the real way to think of the "shippability metric" is as pertaining solely to ship fic with other drivers. although doing more analysis with engineers and principals later down the line could be cool)

also note that since i grouped and summed all fics for every single ship a driver has, and since one fic can be tagged as multiple ships, there will be inevitable overlap/inflation that also lessens the accuracy of the overall number. however, because there's no easy way to discern the presence and overlap of multiship fic for every single driver and every single ship they have, and attempting to do so for a stupid tumblr post would make this an even larger waste of time… just take everything here with a grain of salt!

data for archive overview viz was collected haphazardly over the past few days because i may have procrastinated finishing this post haha. but all ship data for section 2 was specifically collected april 22, 2023.

PART I. f1 rpf archive overview

before i get to ship graphing, here are a few overviews of f1 ficdom growth and where it measures relative to other sports fandoms, since i find the recent american marketability of f1 and its online fandom quite interesting.

first off, here's a graph that shows the cumulative growth of the top 8 sports rpf fandoms from 2011 until now (2023 is obviously incomplete since we're only in may). i've annotated it with some other details, but we can see that f1 experienced major growth after 2019, which is when the first episode of dts was released.

something that fascinated me when making this graph was the recent resurgence of men's football rpf in 2023; while the fandom has remained fairly consistent over the years, i had noticed that its yearly output was on the decline in my old post, and i was especially surprised to see it eclipse even f1 for 2023. turns out that a large driver behind these numbers is its c-fandom, and it reminded me that out of all the sports rpf fandoms, hockey rpf is fairly unpopular amongst chinese sports fans! i wanted to delve into this a little more and look at yearly output trends for the top sports fandoms since 2018, only this time filtered to exclusively english works (a poor approximation for "western" fandom, i know, but a majority of sports fandom on tumblr does create content in english).

another thing i've long been curious about with f1 specifically is—because of how accessible dts and f1 driver marketing are to fans online, does f1 rpf and shipping culture skew a bit more "public" than other fandoms? i'd initially graphed the ratio of public fic on ao3 for hockey because i also wanted to see whether it was on the rise (again, apologies for how many callbacks and references there are in this post to hockey rpf... it's just easy for me to contextualize two familiar sports ficdoms together *__*), but i was surprised to see that it's actually been steadily trending downward for many years now. f1 fic, on the other hand, has steadily been becoming more public since 2016.

another note is that c-ficdom follows different fic-posting etiquette on ao3, and thus chinese-heavy sports rpf fandoms (think table tennis and speed skating) will feature a majority public fic—here's another old graph. since f1 fandom has a relatively larger representation of chinese writers than hockey does, its public ratio falls a little bit if you filter to english-only works, but as of 2023 it remains significantly higher than hockey's!

anyway, onto the actual ship graphing.

my ship collection process yielded 164 ships with 57 drivers, 46 of which have been in f1. all 20 current active f1 drivers have at least one ship with min. 5 fics, though not all of them had a ship that connected them to the 2023 grid. specifically, nyck de vries' only ship at time of collection was with stoffel vandoorne at 56 works.

once again because f1 is so strongly connected, i initially struggled a lot with how i wanted to graph all the ships i'd aggregated—visualizing all of them was just a mess of a million different overlapping edges, not the sprawling tree that branched out more smoothly from players like in hockey. this made me wonder whether it even made sense to graph anything at all... and tbh the jury is still out on whether these are interesting, but regardless here's a visualization of how the current grid is connected (color-coded by team)! i graphed a circular layout and then a "grid-like" layout just for variety lol.

of course, i still wanted to explore how ships with ex-f1 drivers have branched out and show where they connect to drivers on the current grid, especially because not too long ago seb was very much the center of the ficdom ecosystem, and the (based purely on the numbers) segue to today's max/charles split didn't really come to fruition until the dts days. so here's a network of f1 ships with a minimum of 75 works on ao3:

before i go into ship breakdowns, i also have a quick overview of the most "shippable" drivers, aka the drivers with the highest sum of fic from all their respective ships. the second bar chart is color-coded by the count of their unique ships to encapsulate who is more prone to being multi-shipped.

PART II. ship insights

first let's take a look at the most popular f1 ships on ao3, again filtered to driver-only ships.

here's another graph filtered to the current grid only, and then one that shows the 15 ships where one driver isn't and has never been an f1 driver:

for this section, i ended up combining my ship data with a big f1 driver dataset that gave me information on each driver's birth year, points, wins, seasons in f1, nationality... etc., so that's what i'll be using in the rest of the post. disclaimer that i did have to tweak a few things and the data doesn't reflect the most recent races, so please note there might be some slight discrepancies in my visualizations.

anyway—in my hockey post i did a lot of set analysis because i was interested in figuring out what made the players who were part of the ship network different from the general population. with f1, since almost Every Driver has at least one ship and it's a much more representative group, doing a lot of set distributions wasn't that interesting and so i stuck more to pure ship analysis. still, the set isn't completely representative, which i noted by checking the ratios of driver nationalities in my dataset and then in the large database of f1 drivers i merged with (though filtered to debut year >= 2000 to maintain i guess the same "dimensions").

while british and german drivers have been the most common nationalities in f1 since 2000, both in general and in my ship data, it seems that ficdom slightly overrepresents/overships them and then underrepresents brazilian drivers. i was also curious to see the distribution of ships by nationality combination (which is actually quite diverse), and though it once again wasn't surprising that uk/germany was the most common combination given that we've just established the commonality of their driver groups, i found it somewhat interesting to realize just how many ships fall under this umbrella.

i then once again wanted to see what the distribution of age differences looked across ships. the ships i graphed yielded a range of 25 years, with the oldest age difference being 25 years between piastri and webber. tbh, something that's interesting to me about f1 ships is not just how connected current drivers are but also how there is a very strong aspect of cyclicality, wherein long careers in combination with well-established celebrity culture and post-retirement pivots to punditry & mentorship position drivers perfectly to still be easily shipped with any variety of upcoming drivers, hence why we encounter a relatively significant variety of age differences.

of the ships with two f1 drivers, 38% were within 2 years of each other, while 44% had an age difference of 5 years or more.

more experimentally (basically i wanted to use these performance metrics for something!), i tried graphing driver metrics against "shippability" to see whether i could uncover any trends, normalizing to percentile to make it more visually comprehensible.

one thing that was interesting to me is that there is a strong correlation between a driver's points per entry and their number of ship fic; really, this isn't surprising at all because it's basically a reflection of whether they've driven for a big 3 team, and we know that the most popular drivers are from big 3 teams, but then i guess it does become a bit of a chicken and egg question... which is something i'm continuously fascinated by when discussing success and talent in sports fandom, especially in a sport like f1 where there is so little parity and thus "points" do not always quantifiably translate to "talent," making it difficult to gauge why and when a driver's skill becomes consciously appealing to an audience. i don't know but here's that scatterplot.

similarly, i also wanted to look at years active vs. fic to gauge which drivers have a High Number Of Ship Fic relative to how long they've actually been in f1, basically a rough rework of the "shippability above expected" metric i'd tried exploring in my old f1 post haha. because the set i merged with attributed 1 "year active" to a driver just like, filling in as reserve for a single race, and it also included drivers who maybe raced one season and then never raced again, but then i still wanted to include current rookies in their first season to show where their Potential lies... i settled on filtering to drivers who were or have been active for at least 5 seasons OR who debuted recently and thus have a bit of rookie leeway. there's a decent amount of correlation here, which is again... in f1, the underlying argument for remaining active for many years is that you have to be good enough to keep your seat, so it's expected that if drivers stay on the grid for a long time they will eventually accrue more fandom interest and thus ship fic. still, we can see some drivers who underperform a little relative to their establishedness—bot and per, interestingly also below the trend line in the points/entry graph–and then those who overperform a decent amount, like nor and lec.

this is somewhat interesting to me because i'd tried to make a similar scatterplot with my hockey set and found that there was... basically nooo correlation at all, but i also had to make do with draft year and not gp which i think might move the needle a little bit. regardless, it's just interesting to think about these things in the context of league/grid exclusivity and then other further nuances like the possibilities of making your niche in, for example, the nhl as a 4th line grinder or f1 as a de facto but reliable #2 driver for years down the stretch, and then how all of that impacts or shapes your fandom stock and shippability.

moving on, here's a look at the current top 20 f1 ships and how much of their fic is tagged as fluff or angst! out of all their fic, kimi/seb have the highest fluff ratio at 38.44%, while lewis/nico hold the throne for angst at 34.74%.

lewis/nico are also the most "holistically" tragic ship when you subtract their fluff and angst percentages (by a large margin as well), while jenson/seb are the fluffiest with a difference of 17.38%. really makes you think.

and finally this is a dumb iteration from my old f1 post but i thought this was kind of funny haha so: basically what if teammate point share h2h but the points are their shippability on ao3.

closing thoughts

that's really all i have! again, i don't know whether any of these graphs make sense or are interesting to anyone, but i had fun trying to adapt some of my hockey methodology to f1 and also revisiting the old f1 graphs i'd made last year and getting to recalculate/design them. i know there's a lot more i could have done in examining drivers' old teams since many ships are based on drivers being ex-teammates and not the current grid matchups, but it would have been too much of a headache to figure out so... this is the best i've got. thanks for reading :)

[robobarbie]

what is your day job, and how did you get it? I'm sorry if this is too personal. I graduated from university for 6 months now and haven't been able to find a well paying job. I remember you said that your job pays you well and that you had time to pursue your own projects and was wondering if you could share your experience in your education and work experience or any advice to someone who's feeling very lost and confused right now.

i was in a similar boat! i only figured out what field to go into because my ex's uncle mentioned that i should check out "big data", otherwise i was just as wandery. i had a math degree, but no real idea what to do with it.

i got my first job from a career fair at my university, but not from the career fair my department held (you should always go to all career fairs, not just ones held by ur department). obvs you are not in school anymore, so you might want to get more into networking. you can reach out to old classmates/professors to see if they can give you a rec somewhere, or reach out to people at companies directly on sites like linkedin after you apply so they remember you when they see your application. if you aren't making it to the interview stage, you might need to review and redo ur resume. but, i mean, this is all typical advice you can find online.

i am in stats/data analysis for my career, and they take people in entry level jobs if you have some sort of degree about stats/finance/econ/data science/etc for the jobs that pay well. if you want to try data, you can also take a low paying job and build up a data analysis portfolio by tackling problems on kaggle.

It's tough out there right now -- I know a lot of people in your situation. Stay strong gamer.

[altin-studies]

Hey there! I want to go into statistical analysis and comms/data analysis, and I have a pretty good plan in place already and know what I'm doing, but was wondering if there are any tips you could give as I see in your bio you're studying data science?

Anything I should do for prep/classes to take to get me a leg up would be amazing, thank you in advance!

Hey there! Thanks for the ask!

If you're going into stat, the first thing I'd suggest is get a good grip on your maths.(Rhyme not intended lol) You should take courses on Derivatives, Integrals, Linear Algebra. We are also taught Real analysis pretty intensively.

For programming languages, I'd say Python is more than enough. But R, SQL are good to have on your CV. Open up a kaggle account and start doing some work there. It will take you a long way.

The best tip I can give you is to take care of your health. It's a pretty taxing subject once you get into it. But prioritise yourself first. Our coursework is intense and while it might not be the same for you, doing mathematics all day is always difficult.

Good luck on your journey. Hope I was of help.

[ask-dr-kujo]

OOC MOD POST

Alright, since one of you asked, here's how you find out your jojos bizarre adventure stand through an autism assessment test.

First, take the idr labs assessment test.

Next, you take the inage from your results and open the image in a new tab

Go to the image and copy the URL. These are the values you put into the program.

Go to the kaggle notebook and click the button that says "copy and edit."

Go to the second block of code and replace the URL that is in the quotations with the one you just copied.

And hit the run all button (its the two fast forward arrows at the top)

And enjoy your results..

Here are mine.

[agileseen24]

"From Passion to Profession: Steps to Enter the Tech Industry"

How to Break into the Tech World: Your Comprehensive Guide

In today’s fast-paced digital landscape, the tech industry is thriving and full of opportunities. Whether you’re a student, a career changer, or someone passionate about technology, you may be wondering, “How do I get into the tech world?” This guide will provide you with actionable steps, resources, and insights to help you successfully navigate your journey.

Understanding the Tech Landscape

Before you start, it's essential to understand the various sectors within the tech industry. Key areas include:

Software Development: Designing and building applications and systems.

Data Science: Analyzing data to support decision-making.

Cybersecurity: Safeguarding systems and networks from digital threats.

Product Management: Overseeing the development and delivery of tech products.

User Experience (UX) Design: Focusing on the usability and overall experience of tech products.

Identifying your interests will help you choose the right path.

Step 1: Assess Your Interests and Skills

Begin your journey by evaluating your interests and existing skills. Consider the following questions:

What areas of technology excite me the most?

Do I prefer coding, data analysis, design, or project management?

What transferable skills do I already possess?

This self-assessment will help clarify your direction in the tech field.

Step 2: Gain Relevant Education and Skills

Formal Education

While a degree isn’t always necessary, it can be beneficial, especially for roles in software engineering or data science. Options include:

Computer Science Degree: Provides a strong foundation in programming and system design.

Coding Bootcamps: Intensive programs that teach practical skills quickly.

Online Courses: Platforms like Coursera, edX, and Udacity offer courses in various tech fields.

Self-Learning and Online Resources

The tech industry evolves rapidly, making self-learning crucial. Explore resources like:

FreeCodeCamp: Offers free coding tutorials and projects.

Kaggle: A platform for data science practice and competitions.

YouTube: Channels dedicated to tutorials on coding, design, and more.

Certifications

Certifications can enhance your credentials. Consider options like:

AWS Certified Solutions Architect: Valuable for cloud computing roles.

Certified Information Systems Security Professional (CISSP): Great for cybersecurity.

Google Analytics Certification: Useful for data-driven positions.

Step 3: Build a Portfolio

A strong portfolio showcases your skills and projects. Here’s how to create one:

For Developers

GitHub: Share your code and contributions to open-source projects.

Personal Website: Create a site to display your projects, skills, and resume.

For Designers

Design Portfolio: Use platforms like Behance or Dribbble to showcase your work.

Case Studies: Document your design process and outcomes.

For Data Professionals

Data Projects: Analyze public datasets and share your findings.

Blogging: Write about your data analysis and insights on a personal blog.

Step 4: Network in the Tech Community

Networking is vital for success in tech. Here are some strategies:

Attend Meetups and Conferences

Search for local tech meetups or conferences. Websites like Meetup.com and Eventbrite can help you find relevant events, providing opportunities to meet professionals and learn from experts.

Join Online Communities

Engage in online forums and communities. Use platforms like:

LinkedIn: Connect with industry professionals and share insights.

Twitter: Follow tech influencers and participate in discussions.

Reddit: Subreddits like r/learnprogramming and r/datascience offer valuable advice and support.

Seek Mentorship

Finding a mentor can greatly benefit your journey. Reach out to experienced professionals in your field and ask for guidance.

Step 5: Gain Practical Experience

Hands-on experience is often more valuable than formal education. Here’s how to gain it:

Internships

Apply for internships, even if they are unpaid. They offer exposure to real-world projects and networking opportunities.

Freelancing

Consider freelancing to build your portfolio and gain experience. Platforms like Upwork and Fiverr can connect you with clients.

Contribute to Open Source

Engaging in open-source projects can enhance your skills and visibility. Many projects on GitHub are looking for contributors.

Step 6: Prepare for Job Applications

Crafting Your Resume

Tailor your resume to highlight relevant skills and experiences. Align it with the job description for each application.

Writing a Cover Letter

A compelling cover letter can set you apart. Highlight your passion for technology and what you can contribute.

Practice Interviewing

Prepare for technical interviews by practicing coding challenges on platforms like LeetCode or HackerRank. For non-technical roles, rehearse common behavioral questions.

Step 7: Stay Updated and Keep Learning

The tech world is ever-evolving, making it crucial to stay current. Subscribe to industry newsletters, follow tech blogs, and continue learning through online courses.

Follow Industry Trends

Stay informed about emerging technologies and trends in your field. Resources like TechCrunch, Wired, and industry-specific blogs can provide valuable insights.

Continuous Learning

Dedicate time each week for learning. Whether through new courses, reading, or personal projects, ongoing education is essential for long-term success.

Conclusion

Breaking into the tech world may seem daunting, but with the right approach and commitment, it’s entirely possible. By assessing your interests, acquiring relevant skills, building a portfolio, networking, gaining practical experience, preparing for job applications, and committing to lifelong learning, you’ll be well on your way to a rewarding career in technology.

Embrace the journey, stay curious, and connect with the tech community. The tech world is vast and filled with possibilities, and your adventure is just beginning. Take that first step today and unlock the doors to your future in technology!

contact Infoemation wensite: https://agileseen.com/how-to-get-to-tech-world/ Phone: 01722-326809 Email: info@agileseen.com