In this episode, our guest was Balázs Kégl, the head of AI Research at Huawei Paris. We were talking about ML 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.

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 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.

In this episode, our guest is Vladimir Vlasov, a senior ML researcher at Rasa. If you don't know Rasa, it's a company that is building a standard infrastructure for conversational AI. Vladimir works on Rasa's ML-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 the future of...

In this episode, our guest is Eugene Dubossarsky, who is the chief data scientist at AlphaZetta and co-founder at multiple data science companies in Australia. Eugene is dealing with machine learning since the 80s, so he has a very strong opinion about different topics in this industry. We talked about random forests, neural nets, boosting strategies, the importance of understanding data and statistics, and about the effects of the current and upcoming crisis either.

We recorded an extra and special episode, our guest is Marco Lemessi, who is a ML leader at John Deere. He will share some of the business aspects and also problems of the ML projects they are facing at one of the biggest agricultural companies in the world. He has a very broad perspective about business cases and data science and optimization problems. He will talk about precision agriculture, problems of labeling on large dataset, legal aspects of AI.

In this episode our guest is Abhishek Thakur, who is the Chief Data Scientist at Boost.ai in Norway. Abhishek has become the World’s first Quadruple Grandmaster on Kaggle. So we asked him about his experiences of the 150 competitions he has taken part in. So, what are the tricks here? How can someone participate in so many competitions, rank high and have a work besides these? Although he has been so successful you will see that he is a very humble person.

Our guest is Benedek Rozemberczki who is a Data Science Phd candidate at University of Edinburgh and owner of the github repo Karate Club where he implemented more than 30 scientific papers about Graphs and ML. More information: https://bit.ly/3awla0U 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: [email protected]

In this episode our guest is Max Sklar who works as Engineering and Innovation Labs advisor at Foursquare. We talked about ratings and the problems about deciding if a rating is positive or negative, and the problems about different languages. In the second part of the show we talked about marketing attribution and causality.

In this episode, out guest is Diganta Misra, who is the founder of Landskape AI, which is a research lab aimed at solving the most challenging questions of Deep Learning. Diganta is a Mathematician who invented the activation function called MISH, which beats Google's activation function called Swish in most computer vision tasks. He released his paper last summer and the project got already 600 stars on Github. More info: http://bit.ly/2UnSuTp

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. Show notes:...

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.

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.

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.

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/

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