Podcast appearances and mentions of joel grus

Neste episódio do Emílias Podcast, o professor Adolfo Neto da UTFPR Curitiba e Nathálya Chaves, bolsista do Emílias Podcast, entrevistam Juliana Rodrigues, engenheira de dados na Volvo do Brasil. A conversa começa com uma exploração do papel de uma engenheira de dados e do conceito de Data Analytics, baseado na experiência de Juliana na indústria automotiva. Os entrevistadores também mergulham na jornada profissional e educacional de Juliana, desde seu interesse inicial pela computação até sua formação em Engenharia de Computação pela UTFPR. Juliana compartilha suas experiências e desafios como mulher na área e discute a importância de grupos de apoio para mulheres na computação. O episódio termina com os anfitriões agradecendo a presença de Juliana e convidando os ouvintes a conhecerem mais sobre ela em suas redes sociais.  Juliana Rodrigues Viscenheski Data Engineer | Volvo do Brasil Engenheira de Computação pela UTFPR https://www.linkedin.com/in/juliana-rodrigues-viscenheski-02979a136/  Indicações de Juliana: Filme Estrelas Além do Tempo https://www.imdb.com/title/tt4846340 Livro Data Science do Zero, Joel Grus https://www.google.com.br/books/edition/Data_Science_do_Zero/2LZwDwAAQBAJ  Livro Storytelling com Dados, Cole Nussbaumer Knaflic  https://books.google.com.br/books?id=R9pdDwAAQBAJ  Links: Building your skillset with a twist: A student's guide to the future - And my 3 data-driven book recommendations https://medium.com/@juliana.viscenheski/building-your-skillset-with-a-twist-a-students-guide-to-the-future-14a9a06f5d0  Postagem no Linkedin sobre evento na UTFPR https://www.linkedin.com/posts/juliana-rodrigues-viscenheski-02979a136_building-your-skillset-with-a-twist-a-student-activity-7112226232863186944-x2b6?utm_source=share&utm_medium=member_desktop  Livebook: https://livebook.dev/  Entrevistadores: Adolfo Neto https://adolfont.github.io/  Nathalya Chaves https://www.instagram.com/nathalyachavs/  O Emílias Podcast é um projeto de extensão da UTFPR Curitiba. Descubra tudo sobre o programa Emílias - Armação em Bits em ⁠⁠⁠⁠⁠⁠https://linktr.ee/Emilias. #PODCAST #EMILIAS --- Send in a voice message: https://podcasters.spotify.com/pod/show/emilias-podcast/message

We are joined by Software Engineer, Data Scientist, and Author Joel Grus for a discussion that ranges from the latest NLP techniques to thoughts on how to think about complexity costs.

MLOps Coffee Sessions #62 with Joel Grus, MLOps from Scratch. // Abstract In this talk, Joel Grus of “I don't like notebooks” fame shares with us his 2021 perspective on notebooks, where he thinks MLOps is now, and what his hot takes in the data space are now. // Bio Joel Grus is a Principal Engineer at Capital Group, where he leads a team that builds search, data, and machine learning products for the investment group. He is the author of the bestselling O'Reilly book *Data Science from Scratch*, the not-bestselling self-published book *Ten Essays on Fizz Buzz*, and the controversial JupyterCon talk "I Don't Like Notebooks." He recently moved to Texas after living in Seattle for a very long time. // Relevant Links Data Science from Scratch book: https://www.oreilly.com/library/view/data-science-from/9781491901410/ Data Science from Scratch, 2nd Edition book: https://www.oreilly.com/library/view/data-science-from/9781492041122/ Ten Essays on Fizz Buzz: Meditations on Python, mathematics, science, engineering, and design book: https://www.amazon.com/Ten-Essays-Fizz-Buzz-Meditations/dp/0982481829 or https://leanpub.com/fizzbuzz/ I Don't Like Notebooks talk: https://www.youtube.com/watch?v=7jiPeIFXb6U I Don't Like Notebooks - #JupyterCon 2018 slides: https://docs.google.com/presentation/d/1n2RlMdmv1p25Xy5thJUhkKGvjtV-dkAIsUXP-AL4ffI/edit#slide=id.g362da58057_0_658 Fizz Buzz in Tensorflow: https://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Joel on LinkedIn: https://www.linkedin.com/in/joelgrus/ Timestamps: [00:00] Introduction to Joel Grus [01:32] Joel's background in tech [07:47] Joel's I Don't Like Notebooks talk on Jupyter Con [13:42] Better tooling around notebooks [16:48] Hex [17:20] Step function evolution [20:41] Kinds of professionals required in Joel's organization to practice MLOps [23:08] Evaluation process [25:51] Sagemaker bring your own algorithm [27:30] Flexibility of models [31:55] Hot takes on data science world [34:19] Current Overall Maturity of MLOps [37:23] Kinds of problem in NLP and search [39:52] Finding ways to put structures [40:50] Probabilistic nature of machine learning systems [43:10] Data scientists coping up on writing production code [46:33] Invaluability of code review [47:22] Common repo structure [47:57] Reviewing codes [49:15] Code pals [50:36] Readability and function [52:23] Leverage code review [53:10] Remote work

Mit Christian haben wir uns heute mal wieder ein bisschen mehr über Machine Learning etc. unterhalten. Was wäre, wenn man Jupyter-Notebooks als IDE verwenden wollte (nbdev)? Was braucht man eigentlich heutzutage so an Hardware, wenn man Modelle trainieren will? Ausserdem haben wir ein bisschen auf der Mikrofon/Headset-Seite aufgerüstet (keine Ahnung, ob man das hört).     Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de News aus der Szene Numpy 1.20 Release Pandas 1.2 Release Spacy v3 Release Ben Gorman: Python NumPy For Your Grandma, Python Pandas For Your Grandpa Mypy 0.800 Release Pip 21.0 Release appenv, batou NBDEV nbdev I don't like notebooks.- Joel Grus Literate Programming I Like Notebooks - Jeremy Howard google colab Binder Buch: Deep Learning for Coders with fastai and PyTorch Machine Learning Recap ocr: Tesseract Vektorrechner / Tensor Cores / TPUs Hardware: Which GPU(s) to Get for Deep Learning Criteo: Display Advertising Challenge Netflix Prize Öffentliches Tag auf konektom

Researches and others using data science and software need to follow solid software engineering practices. This is a message that Joel Grus has been promoting for some time. Joel joins the show this week to talk about data science, software engineering, and even Fizz Buzz. Topics include: Software Engineering practices and data science Difficulties with Jupyter notebooks Code reviews on experiment code Unit tests on experiment code Finding bugs before doing experiments Tests for data pipelines Tests for deep learning models Showing researchers the value of tests by showing the bugs found that wouldn't have been found without them. "Data Science from Scratch" book Showing testing during teaching Data Science "Ten Essays on Fizz Buzz" book Meditations on Python, mathematics, science, engineerign and design Testing Fizz Buzz Different algorithms and solutions to an age old interview question. If not Fizz Buzz, what makes a decent coding interview question. pytest hypothesis Math requirements for data science Special Guest: Joel Grus.

Subscribe: iTunes, Android, Spotify, Stitcher, Google, and RSS.In this episode of the Data Exchange I speak with Joel Grus, Principal Engineer at the Capital Group. He previously served as a Senior Research Engineer at the Allen Institute for AI, where he was a core engineer on AllenNLP, a PyTorch-based library for NLP research. Joel is also the author of one of the most widely read books in data science – Data Science from Scratch. Joel has a new book which I recommend highly:  Ten Essays on Fizz Buzz.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

In this episode, Joel and I discuss his brilliant new book Ten Essays on Fizz Buzz Meditations on Python, mathematics, science, engineering, and design.

Hadelin first spoke about his entrepreneurship mindset. This was already in his mind early on, and never left him. We spoke about his interest for math, science and technology and how it drove him to one of the best French engineering schools. We talked about him discovering Data Science and how he changed his major at the very last minute. We then talked about his professional life. How he got to work at google, and why he didn't reconduct his contract and chose to create online courses instead. We spoke about his further business ventures, all the way to BlueLife, his current company. We finished talking about AI as a whole and finding your purpose to do good in life.Hadelin is the co-founder and CEO of BlueLife AI which leverages AI for optimizing processes, maximizing efficiency and increasing profitability. Hadelin is also an online entrepreneur, who has created educational e-courses about Machine Learning, Deep Learning, AI and Blockchain, which have reached over half a million customers worldwide.Here are the links of the show:LinkedIn https://www.linkedin.com/in/hadelin-de-ponteves-1425ba5bTwitter https://www.twitter.com/hadelin2pCourse https://www.udemy.com/course/machinelearningBook "AI Crash Course" https://www.amazon.com/Crash-Course-hands-introduction-reinforcement/dp/1838645357?Conference https://www.datasciencego.comReinforcement Learning by Francis Bach, Richard S. Sutton & Andrew G. Barto https://amzn.to/2TczFk5Data Science from Scratch by from Joel Grus https://amzn.to/2vVsggWData Science for Business by Foster Provost & Tom Fawcett https://amzn.to/2SVMcJPPython Machine Learning by Sebastian Raschka & Vahid Mirjalili https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750?The big leap by Gay Hendricks https://amzn.to/2v9LzDcMillionaire Success Habits by Dean Graziozi https://amzn.to/2vYdyWqCreditsMusic Aye by Yung Kartz is licensed CC BY-NC-ND 4.0.Your hostSoftware Developer‘s Journey is hosted and produced by Timothée (Tim) Bourguignon, a crazy frenchman living in Germany who dedicated his life to helping others learn & grow. More about him at timbourguignon.fr.Gift the podcast a ratingPlease do me and your fellow listeners a favor by spreading the good word about this podcast. And please leave a rating (excellent of course) on the major podcasting platforms, this is the best way to increase the visibility of the podcast:Apple PodcastsStitcherGoogle PlayPatreonFinally, if you want to help produce the podcast, support me on

Dans cet épisode, je discute avec Jean-Thomas Baillargeon de son parcours vers la science des données et l'apprentissage machine, de son intérêt pour l'informatique et de ses objectifs futurs. Les livres qu'il nous recommande : Head first design patterns : https://www.amazon.ca/Head-First-Design-Patterns-Brain-Friendly/dp/0596007124 Data science from scratch : https://www.amazon.ca/Joel-Grus/dp/1492041130/ref=sr_1_1?__mk_fr_CA=%C3%85M%C3%85%C5%BD%C3%95%C3%91&keywords=data+science+from+scratch&qid=1565225253&s=books&sr=1-1 Merci à mes commanditaires l'AÉLIES (https://www.aelies.ulaval.ca), le SPLA (https://www.spla.ulaval.ca), l'AGIL (http://www2.ift.ulaval.ca/~agil/) et .Layer (https://www.dotlayer.org/). Abonne-toi au podcast!

Dans cet épisode, je discute avec Jean-Thomas Baillargeon de son parcours vers la science des données et l’apprentissage machine, de son intérêt pour l’informatique et de ses objectifs futurs. Les livres qu'il nous recommande :nbsp;- Head first design patterns : https://www.amazon.ca/Head-First-Design-Patterns-Brain-Friendly/dp/0596007124- Data science from scratch : https://www.amazon.ca/Joel-Grus/dp/1492041130/ref=sr_1_1?__mk_fr_CA=%C3%85M%C3%85%C5%BD%C3%95%C3%91amp;keywords=data+science+from+scratchamp;qid=1565225253amp;s=booksamp;sr=1-1 Merci à mes commanditaires l'AÉLIES (https://www.aelies.ulaval.ca), le SPLA (https://www.spla.ulaval.ca), l’AGIL (http://www2.ift.ulaval.ca/~agil/) et .Layer (https://www.dotlayer.org/) . Abonne-toi au podcast !--------[Facebook] - https://www.facebook.com/OpenLayerPodcast/[Spotify] - https://open.spotify.com/show/6LWUHrtNrRioE7Ggxkpcno?si=-3tZo88XSnW0Mwcp7sZkLA[Balado Québec] - https://baladoquebec.ca/#!/openlayer[iTunes Podcast] - https://podcasts.apple.com/ca/podcast/openlayer/id1477641065[Google Play Music] - https://playmusic.app.goo.gl/?ibi=com.google.PlayMusicamp;isi=691797987amp;ius=googleplaymusicamp;apn=com.google.android.musicamp;link=https://play.google.com/music/m/Iytw3gkywmyegzfe45u2y5ciqfm?t%3DOpenLayer%26pcampaignid%3DMKT-na-all-co-pr-mu-pod-16[Google Podcast] – OpenLayer Podcast------

We’re talking with Joel Grus, author of Data Science from Scratch, 2nd Edition, senior research engineer at the Allen Institute for AI (AI2), and maintainer of AllenNLP. We discussed Joel’s book, which has become a personal favorite of the hosts, and why he decided to approach data science and AI “from scratch.” Joel also gives us a glimpse into AI2, an introduction to AllenNLP, and some tips for writing good research code. This episode is packed full of reproducible AI goodness!

We’re talking with Joel Grus, author of Data Science from Scratch, 2nd Edition, senior research engineer at the Allen Institute for AI (AI2), and maintainer of AllenNLP. We discussed Joel’s book, which has become a personal favorite of the hosts, and why he decided to approach data science and AI “from scratch.” Joel also gives us a glimpse into AI2, an introduction to AllenNLP, and some tips for writing good research code. This episode is packed full of reproducible AI goodness!

To most data scientists, the jupyter notebook is a staple tool: it’s where they learned the ropes, it’s where they go to prototype models or explore their data — basically, it’s the default arena for their all their data science work. But Joel Grus isn’t like most data scientists: he’s a former hedge fund manager and former Googler, and author of Data Science From Scratch. He currently works as a research engineer at the Allen Institute for Artificial Intelligence, and maintains a very active Twitter account. Oh, and he thinks you should stop using Jupyter noteoboks. Now. When you ask him why, he’ll provide many reasons, but a handful really stand out: Hidden state: let’s say you define a variable like a = 1 in the first cell of your notebook. In a later cell, you assign it a new value, say a = 3 . This results is fairly predictable behavior as long as you run your notebook in order, from top to bottom. But if you don’t—or worse still, if you run the a = 3 cell and delete it later — it can be hard, or impossible to know from a simple inspection of the notebook what the true state of your variables is. Replicability: one of the most important things to do to ensure that you’re running repeatable data science experiments is to write robust, modular code. Jupyter notebooks implicitly discourage this, because they’re not designed to be modularized (awkward hacks do allow you to import one notebook into another, but they’re, well, awkward). What’s more, to reproduce another person’s results, you need to first reproduce the environment in which their code was run. Vanilla notebooks don’t give you a good way to do that. Bad for teaching: Jupyter notebooks make it very easy to write terrible tutorials — you know, the kind where you mindlessly hit “shift-enter” a whole bunch of times, and make your computer do a bunch of stuff that you don’t actually understand? It leads to a lot of frustrated learners, or even worse, a lot of beginners who think they understand how to code, but actually don’t. Overall, Joel’s objections to Jupyter notebooks seem to come in large part from his somewhat philosophical view that data scientists should follow the same set of best practices that any good software engineers would. For instance, Joel stresses the importance of writing unit tests (even for data science code), and is a strong proponent of using type annotation (if you aren’t familiar with that, you should definitely learn about it here). But even Joel thinks Jupyter notebooks have a place in data science: if you’re poking around at a pandas dataframe to do some basic exploratory data analysis, it’s hard to think of a better way to produce helpful plots on the fly than the trusty ol’ Jupyter notebook. Whatever side of the Jupyter debate you’re on, it’s hard to deny that Joel makes some compelling points. I’m not personally shutting down my Jupyter kernel just yet, but I’m guessing I’ll be firing up my favorite IDE a bit more often in the future.

This week’s episode is a special one, as we’re welcoming a guest: Joel Grus is a data scientist with a strong software engineering streak, and he does an impressive amount of speaking, writing, and podcasting as well. Whether you’re a new data scientist just getting started, or a seasoned hand looking to improve your skill set, there’s something for you in Joel’s repertoire.

On this episode, Andrew is joined by Joel Grus (@joelgrus), the author of Data Science from Scratch, whose second edition just came out. They discuss spreadsheets, writing a book, Python 3, type annotations, Jupyter notebooks, reproducibility, and what it's like to do standup comedy at a conference that has a code of conduct. Please listen to it.

Episode 1 Show Notes (http://pythonoutloud.com/1): pythonoutloud.com/1 (http://pythonoutloud.com/1) These show note were written on the Shinano Train, on Kevin's smartphone, steaming toward the Snow Monkey Park in Nagano, Japan. In Episode 1, we discuss the infamous programming challenge known as FizzBuzz (no space), Fizz Buzz (with a space), or fizz_buzz (in PEP8-friendly syntax). We start off with its origin story, a math game used to teach children division. We then debate whether sitting around in a circle and taking turns saying “one, two, fizz, four, buzz, ...” is as fun in the digital age. Even less fun? Fizz Buzz's reputation as a job interview question. For more about this version, see the well-known blog post by Jeff Atwood at https://blog.codinghorror.com/why-cant-programmers-program/ (https://blog.codinghorror.com/why-cant-programmers-program/) We're still not sure whether Fizz Buzz, or any other math-heavy question, is suitable for determining someone's capacity as a programmer. But as a learning tool, Fizz Buzz does provide a compact way of demonstrating a wide range of programming topics, including variables, conditionals, and loops. The only downside is also needing to learn modular arithmetic: https://nrich.maths.org/4350 (https://nrich.maths.org/4350) And if you need even more math in your Fizz Buzz solution, look no further than this blog post by Joel Grus: http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/ (http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/) Rounding out the episode, we share some project updates, including Kevin's recent Medium article on "Automating Surveys with Python, Qualtrics API and Windows Task Scheduler": https://medium.com/@changkevin/automating-surveys-with-python-qualtrics-api-and-windows-task-scheduler-4bffc58726d7 (https://medium.com/@changkevin/automating-surveys-with-python-qualtrics-api-and-windows-task-scheduler-4bffc58726d7) Neither of us is affiliated with Qualtrics in any way, but we did publish a qualtrics-mailer package on PyPI a few months ago: https://pypi.python.org/pypi/qualtrics-mailer/0.1 (https://pypi.python.org/pypi/qualtrics-mailer/0.1) This episode features the song "Happy Ukulele" by Scott Holmes (http://freemusicarchive.org/music/Scott_Holmes/) and the songs "And So Then", "Curiousity", "Manhattan By Moonlight" and "Puzzle Pieces" by Lee Rosevere (http://freemusicarchive.org/music/Lee_Rosevere/). Thank you for your support, and stay tuned for Episode 2. We plan to continue discussing problem solving in Python, focusing on http://www.pythonchallenge.com/ (http://www.pythonchallenge.com/). If you’re still reading, there's a statistically significant chance you want to help us build a community and support our cause! If our prediction is correct, please visit pythonoutloud.com/donate (http://pythonoutloud.com/donate). We want Python Out Loud to be community driven and non-profit oriented, which is why we pledge to be transparent and donate anything in excess of our operating expenses to the Python Software Foundation (PSF). For just $3, we'll even mail you a limited-edition Python Out Loud sticker!

A conversation about religion and falsity with Joel Grus, humorist, atheist and author of Your Religion is False.