Machine Learning Cafe – Détails, épisodes et analyse

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Machine Learning Cafe

Miklos Zoltan Toth

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Business & Entrepreneuriat

Fréquence : 1 épisode/25j. Total Éps: 16

This is a machine-learning-focused Podcast, where we interview people in the field of Artificial Intelligence and discuss interesting technical topics of Machine Learning. In the episodes, we focus on business-related use-cases (especially with Deep Learning ) and we also try to bring some technical white papers to the ground, not forgetting on the way that there are always some people behind the technology, so we try to understand their motivation and drive.

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🇬🇧 Grande Bretagne - mathematics
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🇺🇸 États-Unis - mathematics
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🇫🇷 France - mathematics
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🇬🇧 Grande Bretagne - mathematics
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🇩🇪 Allemagne - mathematics
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🇫🇷 France - mathematics
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https://filmmusic.io
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https://incompetech.com
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What is beyond PoCs? ML project-hurdles you should be prepared to take with Balázs Kégl - 016

Épisode 16

lundi 14 décembre 2020 • Durée 56:20

In this episode, our guest was Balázs Kégl who is head of AI Research at Huawei Paris. We were talking about machine learning projects from the organizational point of view. We talked about the relationship between technical and non-technical people, and why are there so many POC projects why only a few of them is productionised? So, if you are a data scientist and you cannot convince your manager or your client to create a project, you should definitely listen to this episode. Moreover, we talked about some technical stuff at the end.

Connect Balazs at Linkedin:
https://www.linkedin.com/in/balazskegl/

---General Info---
About the host: Miklos Toth is a Machine Learning enthusiast who is also teaching Machine Learning and Deep Learning at international companies, working on various ML projects as an engineer.

About the co-host: Levente Szabados is a Deep tech leader, consultant, and manager with a special interest in artificial intelligence, cognitive sciences, data science, and deep learning. He is teaching various ML courses at the Frankfurt School of Finance and Management.

About the podcast: The podcast was created to serve the technical community with details of ML algorithms, use-cases. The episodes are recorded in Budapest, Hungary, Europe.

Website of the podcast: http://machinelearningcafe.org/
Host's LinkedIn: https://www.linkedin.com/in/miklostoth/
Co-Host LinkedIn: https://www.linkedin.com/in/levente-szabados-76334728/
Write an email to the host: miklos@machinelearningcafe.org
Equipment: On the host's side the show is recorded with Rode Procaster and with Roland Quad-Capture audio interface.

---Copyright Info---
Music is from https://filmmusic.io, intro first part is by Miklos Toth and some free garage band loops. :) intro second part: "Aces High" by Kevin MacLeod, outro "Acid Trumpet" by Kevin MacLeod (https://incompetech.com), License: CC BY (http://creativecommons.org/licenses/by/4.0/)

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Alternative Learning Methods 01 with Dmitry Krotov - 015

Épisode 15

mercredi 9 septembre 2020 • Durée 57:17

We continue with a micro-series, where we talk about alternative learning methods. In the first episode of this micro-series our guest is Dmitry Krotov, Researcher at MIT-IBM Watson AI Lab and IBM Research in Cambridge. Among many things, we will talk about an unusual learning rule, which has a degree of biological plausibility.

Find Dmitry on Linkedin: https://www.linkedin.com/in/krotovdmitry/

Paper: Unsupervised Learning by Competing Hidden Units
https://arxiv.org/abs/1806.10181

Paper: Local Unsupervised Learning for Image Analysis:
https://arxiv.org/abs/1908.08993

About the podcast: The podcast was created to serve the technical community with details of ML algorithms, use-cases. The episodes are recorded in Budapest, Hungary, Europe.

Website of the podcast: http://machinelearningcafe.org/
Host's LinkedIn: https://www.linkedin.com/in/miklostoth/
Co-Host LinkedIn: https://www.linkedin.com/in/levente-szabados-76334728/
Write an email to the host: miklos@machinelearningcafe.org Equipment: On the host's side the show is recorded with Rode Procaster and with Roland Quad-Capture audio interface.

Finding the label errors with Cleanlab with Curtis Northcutt - 006

Épisode 6

jeudi 23 janvier 2020 • Durée 55:44

In this episode, I talked with Curtis Northcutt about his application cleanlab, with which you can find label errors in your dataset. Cleanlab computes cross-validated probabilities, the confident joint, and the statistics used in uncertainty estimation for dataset labels, and it ranks and sorts the labels by the probabilities of error, so you can easily find them in your dataset.

Curtis' website:
https://www.curtisnorthcutt.com/

Curtis on LinkedIn:
https://www.linkedin.com/in/cgnorthcutt/

Cleanlab on GitHub:
https://github.com/cgnorthcutt/cleanlab

Cleanlab's blog:
https://l7.curtisnorthcutt.com/cleanlab-python-package

White Papers:
https://arxiv.org/abs/1911.00068
https://arxiv.org/abs/1705.01936

Music by Curtis (PomDP the PhD rapper):
https://soundcloud.com/thephdrapper/bars-on-bars
https://soundcloud.com/thephdrapper/crown
https://soundcloud.com/thephdrapper/dub-dub
https://open.spotify.com/album/2Fjg3zF8PGEg9WWNoeyx3X

---General Info---

Podcast's Website: Host's LinkedIn: Email of the host: miklos@machinelearningcafe.org

A New Deep Learning Optimizer called Ranger with Less Wright - 005

Épisode 5

mardi 7 janvier 2020 • Durée 36:54

In this episode, we interview Less Wright, who has set 13 records on the fast.ai leaderboard, and we talk about one of his tricks is using cutting-edge optimizers, he also developed one, which is called Ranger. He talks also about different strategies of using Deep Learning optimizers, it is worth to take those into consideration. We also tested Ranger and in our case, it also outperformed Adam/RAdam variants. Co-host is Levente Szabados.

Less' LinkedIn: https://www.linkedin.com/in/less-wright-22b59017/

Ranger's article: https://medium.com/@lessw/how-we-beat-the-fastai-leaderboard-score-by-19-77-a-cbb2338fab5c

Less' medium articles: https://medium.com/@lessw

His article about Mixnet he mentions: https://medium.com/@lessw/meet-mixnet-google-brains-new-state-of-the-art-mobile-ai-architecture-bd6c37abfa3a

His Github: https://github.com/lessw2020

Co-Host: Levente Szabados
https://www.linkedin.com/in/levente-szabados-76334728/

---General Info---

Podcast's Website: http://machinelearningcafe.org/
Host's LinkedIn: https://www.linkedin.com/in/miklostoth/
Email of the host: miklos@machinelearningcafe.org

---Copyright Info--- Music is from , intro first part is by Miklos Toth and some free garage band loops. :) intro second part: "Aces High" by Kevin MacLeod, outro "Acid Trumpet" by Kevin MacLeod (), License: CC BY (http://creativecommons.org/licenses/by/4.0/)

Cutting-Edge Gradient Descent Variants with Levente Szabados - 004

Épisode 4

jeudi 19 décembre 2019 • Durée 36:40

In this episode, we are focusing on Deep Learning Optimizers, the different Gradient Descent Variants from vanilla GD to RAdam and Ranger. Levente tells us the story of GD from the simplest ones to the newest ones. This is part 1.

Levente's Linkedin URL:
https://www.linkedin.com/in/levente-szabados-76334728/

An overview of gradient descent optimization
algorithms:
https://arxiv.org/pdf/1609.04747.pdf

Lookahead optimizer algorithm:
https://arxiv.org/pdf/1907.08610.pdf

Ranger optimizer by Less Wright:
https://medium.com/@lessw/new-deep-learning-optimizer-ranger-synergistic-combination-of-radam-lookahead-for-the-best-of-2dc83f79a48d

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Tree-based methods for procurement & are expert systems alive? with Renee Ahel - 003

Épisode 3

jeudi 12 décembre 2019 • Durée 38:08

In this episode, we interview Renee Ahel, who is a Lead AI Expert at Cirtuo and a freelance data scientist in Croatia. He is dealing with Machine Learning since 2003, and recently he is working on Tree-based methods, with which he solves procurement problems. He is also dealing with Expert systems, you will hear why.
His motto is: You should choose the most efficient tool for the problem you have, regardless of whether it is in fashion or not.

AI applications in procurement:
https://sievo.com/resources/ai-in-procurement

Expert systems
https://en.wikipedia.org/wiki/Expert_system

Kraljic matrix
https://www.forbes.com/sites/jwebb/2017/02/28/what-is-the-kraljic-matrix/#2d004b38675f

Bayes network powered PCOS (polycystic ovary syndrome) diagnostics - implemented by Croatian company Vingd for the Berlin startup Clue:
https://techcrunch.com/2019/09/04/period-app-clue-hopes-to-predict-if-you-have-pcos/

Renee Ahel on LinkedIn:
https://www.linkedin.com/in/reneeahel/

Data Science Croatia:
https://www.meetup.com/DataScienceCroatia/

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Can we predict the accuracy of a Neural Network? Yes, with the WeightWatcher tool by Charles Martin, Ph.D. - 002

Épisode 2

samedi 7 décembre 2019 • Durée 56:51

In this Episode we talked about the deep neural networks and the spectral density of each layer's weights. It turns out, you can predict the accuracy ( and many more) with the WeigthWatcher application. We talk about the 5+1 Phases of learning and Heavy Tailed Self Regularization.

Charles Martin, PhD on LinkedIn:
https://www.linkedin.com/in/charlesmartin14/

During the episode we talked about these
VC Theory:
https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_theory

Why Deep Learning works? post by Charles Martin, 2015
https://calculatedcontent.com/2015/03/25/why-does-deep-learning-work/

Presentation at Berkeley: Why Deep Learning works? by Charles Martin, 2016
https://www.youtube.com/watch?v=fHZZgfVgC8U

Several Papers written by Charles Martin and Michael Mahoney:
https://arxiv.org/search/?query=%22Charles+H.+Martin%22&searchtype=author&abstracts=show&order=-announced_date_first&size=50

Newest blog post about weightwatcher, 2019 December:
https://calculatedcontent.com/2019/12/03/towards-a-new-theory-of-learning-statistical-mechanics-of-deep-neural-networks/

WeightWatcher on GitHub:
https://github.com/CalculatedContent/WeightWatcher
easy installation for python users:
pip install weightwatcher

How to reach out:
https://calculationconsulting.com/
charles@calculationconsulting.com

To access their slack channel please contact Charles first.

---Copyright Info---
Music is from https://filmmusic.io , intro first part is by Miklos Toth and some free garage band loops. :) intro second part: "Aces High" by Kevin MacLeod, outro "Acid Trumpet" by Kevin MacLeod (https://incompetech.com), License: CC BY (http://creativecommons.org/licenses/by/4.0/)

Progressively Growing GANs & Style GANs with Alexandr Honchar - 001

Épisode 1

samedi 7 décembre 2019 • Durée 46:56

In this episode, we talked about the idea behind GANs in general and two special types of GANs: Progressively Growing GANs and Style GANs.

LinkedIn URL of Alexandr Honchar:
https://www.linkedin.com/in/alexandr-honchar-4423b962/

Companies where Alex works:
http://neurons-lab.com/
http://mawi.band/

Facebook:
https://www.facebook.com/rachnogstyle

Blog about ECG pipeline:
https://medium.com/mawi-band/towards-ai-based-only-biosignal-analysis-pipeline-39e6e31244a6

Progressively Growing GANs:
https://www.youtube.com/watch?v=G06dEcZ-QTg
https://arxiv.org/abs/1710.10196

StyleGAN:
https://arxiv.org/abs/1812.04948
https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/

---Copyright Info---
Music is from https://filmmusic.io , intro first part is by Miklos Toth and some free garage band loops. :) intro second part: "Aces High" by Kevin MacLeod, outro "Acid Trumpet" by Kevin MacLeod (https://incompetech.com), License: CC BY (http://creativecommons.org/licenses/by/4.0/)

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"Monkeys with laptops" - the (hi)story of applying AI in real life with Denis Rothman - 014

Épisode 14

mardi 23 juin 2020 • Durée 01:14:29

In this "extra" episode, our guest is Denis Rothman, who is an Artificial Intelligence Specialist. He has spent 40 years in this industry, he is the author of the book called "Artificial Intelligence by Example". He is a teacher, a speaker, and he is an exceptional expert.
He has a very special view on what software development is and how to build-up systems that may or may not use artificial intelligence. We believe he is one of the brightest minds we have ever talked to. I think if you listen to this episode you will have new viewpoints of the world.

LinkedIn: https://www.linkedin.com/in/denis-rothman-0b034043/

Latest Book: https://www.amazon.com/Artificial-Intelligence-Example-advanced-learning/dp/1839211539/ref=sr_1_1?dchild=1&keywords=Artificial+Intelligence+by+Example&qid=1591873875&sr=8-1

About the podcast: The podcast was created to serve the technical community with details of ML algorithms, use-cases. The episodes are recorded in Budapest, Hungary, Europe.

Website of the podcast: http://machinelearningcafe.org/
Host's LinkedIn: https://www.linkedin.com/in/miklostoth/
Co-Host LinkedIn: Write an email to the host: miklos@machinelearningcafe.org Equipment: On the host's side the show is recorded with Rode Procaster and with Roland Quad-Capture audio interface.

Conversational AI at Rasa with Vladimir Vlasov - 013

Épisode 13

mercredi 10 juin 2020 • Durée 01:03:24

In this episode, our guest was Vladimir Vlasov, who is a senior machine learning researcher at Rasa. If you don't know Rasa, it's a company that is building a standard infrastructure for conversational AI. Vladimir is working on Rasa's machine learning-based dialogue tools which allow developers to automate contextual conversations.
So we were talking about state-of-the-art area of conversational AI and NLP, also briefly how Rasa’s framework is built up, and what are the technical problems to be solved, and what is the future of conversational AI.

Vladimir on LinkedIn: https://www.linkedin.com/in/vladimir-vv-vlasov/

Rasa: https://rasa.com/

Conference on 18th June: https://www.l3-ai.dev

YouTube channel: https://www.youtube.com/channel/UCJ0V6493mLvqdiVwOKWBODQ

Algorithm Whiteboard: https://www.youtube.com/watch?v=wWNMST6t1TA&list=PL75e0qA87dlG-za8eLI6t0_Pbxafk-cxb

Using Neural Networks to Find Answers in Tables:
https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html

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Machine Learning Cafe – Détails, épisodes et analyse

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