Data Science Digest

2021-04-07 18:05:04 ​​Hey folks,

I need to ask you something.

We plan to launch a series of free webinars in May, and I’d appreciate if you shared your thoughts on what topics we should focus first thing. What are you interested in the most? — Feel free to share as many topics/ideas as you like in the comments' section!

P. S. We’re also looking for speakers. So, if you have some insights to share with the community, we’re ready to provide the platform. Kindly ping me (@dmitryspodarets) to discuss next steps and details.

Thank you!

Best regards,
Dmitry Spodarets
@DataScienceDigest Open / Comment

2021-04-07 10:00:10 ​​Zero-Shot Text-to-Image Generation

One of the major tasks of text-to-image generation is finding better modeling assumptions for training on a fixed dataset. Such assumptions involve complex architectures, auxiliary losses, or side information that make it more difficult to handle data. Discover a new method of text-to-image generation based on a transformer that autoregressively models the text and image tokens as a single stream of data. Learn why it is competitive for at-scale data compared to previous domain-specific models when evaluated in a zero-shot fashion.

https://bit.ly/3fNxuQI

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2021-04-06 10:00:07 ​​Awesome AI-ML-DL Repository

If you are interested in all things AI/ML/Dl as we are, check out this awesome repository by @neomatrix369 that features study notes and a curated list of helpful resources on the topic. The repo is created by and for engineers, developers, data scientists, and all other professionals looking to sharpen their mastery of AI, ML, and DL. As with other repos on GitHub, you can easily contribute to, watch, star, fork, and share the repo with others in the tech community.

https://bit.ly/3upveTN Open / Comment

2021-04-05 12:05:34 Unit Testing Python Code in Jupyter Notebooks

Writing unit tests appears to be the natural way of doing things when you work on production code, library code, or have to engage with test-driven environments. But what about writing unit tests for your Jupyter notebooks? In some cases, you don’t need it, but when you go beyond data exploration, you definitely do — you just have to maintain and monitor your code in the long run. Learn how unit tests can help you make your Data Science work more efficient, and then try it in practice. You won’t be disappointed!

https://bit.ly/3sPHr3W

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2021-04-02 10:00:05 Mixing Normal Images and Adversarial Images When Training CNNs

This tutorial will guide you on the path to Computer Vision mastery. As the first step, you will learn how to generate image batches of normal images and adversarial images during the training process to improve your model’s ability to generalize and defend against adversarial attacks. The tutorial is extra detailed and features all the details that CNN beginners may need to start to prepare images for their networks.

https://bit.ly/39yTvPg

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2021-04-01 20:06:36 R vs. Python vs. Julia

Data Scientists are used to writing code with R and Python, but a new programming language for Data Science, Julia, can be their new option of choice. Julia promises C-like performance without compromising the way Data Scientists write code and interact with data, and brings a refreshing programming mindset to the DS community. Eager to learn more about Julia and how it stands against R and Python? Check out this article!

https://bit.ly/3maeaOR

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2021-03-31 17:15:18 17 Types of Similarity and Dissimilarity Measures Used in Data Science

Measuring similarity and dissimilarity is quite important in any data science work, simply because it allows to “see” how close or distant data objects are located to each other. In this article, the author took a deep dive into 17 types of similarity and dissimilarity measures, to help you navigate various metrics and their applications in Data Science. The amount of work done by the author is massive, so the article is definitely worth checking out.

https://bit.ly/3ub1YQM

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2021-03-30 10:35:16 Recommendation Algorithms & System Designs of YouTube, Spotify, Airbnb, Netflix, TikTok, and Uber

Ever wondered how top technology companies can so accurately predict their customers’ next step? Go no further! In this article, you will find a collection of recommendation algorithms and system designs that they may be using. Note that the author collected all the info from open sources and judges from his personal experience; there is no guarantee that the designs are 100% correct.

https://bit.ly/3m0y0Mo

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2021-03-29 21:31:35 Hey folks!

I know, I know… You’ve all been wondering for quite a while if this digest is actually dead or kinda not exactly. I’m here to say that it’s alive and kicking, and I’m — finally — back.

Now, it makes sense to explain where I’ve been all this time. Well, I have two words for you: Covid-19 and a child (probably shouldn’t use these in one sentence).

With quarantines imposed, most people had to sit at home and watch TV series… But it wasn’t and isn’t the case if you’re a developer. Just WFH, baby. And to the babies — my son was born in May 2020, so my plate was extra full.

Anyway, the situation has stabilized, and I’m back. So, what can you expect to see next?
- Daily updates on all things AI/ML/Data on Telegram and other social media
- Weekly email newsletter with Top AI news and resources
- Quizzes and surveys
- Webinars
- Fun stuff
- etc

I hope this sorted things out a bit. Stay tuned and have a great one, all!

P. S. Updates will start arriving soon.

Best regards,
Dmitry Spodarets
@DataScienceDigest Open / Comment

2020-12-15 19:30:38 Druid - Interactive Analytics At Scale

Druid – один из полезных и популярных инструментов в мире Больших Данных. Именно эта OLAP система позволяет эффективно обрабатывать, хранить и запрашивать данные. Что и подтверждает востребованность Druid среди инструментов в среде обработки Больших Данных
С Владимиром Иордановым мы поговорим о том, как работает Druid, из чего он состоит и каковы его возможности. Владимир познакомит нас с компонентами Druid, расскажет об архитектуре кластера, о том как проходит обработка данных. Мы сможем понаблюдать на практике, как с ним работать.

О спикере:
Владимир Иорданов – BigData Tech Lead в Lohika и уже более шести лет работает в проектах, связанных с Большими Данными. Его последний проект был непосредственно связан с Apache Druid. Во время работы на этом проекте Владимир получил большой опыт работы с поддержкой и разработкой под эту систему в production. Этим опытом наш спикер и поделится на BigData Odesa #TechTalks .

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Когда: 17.12.2020 в 19:00
Язык доклада: русский
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Как всегда, вас ждет интересная тема и полезный вечер. Вы научитесь эффективно применять Druid в своих проектах при построении системы обработки больших данных.

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