Blog "New Yorko Times"

Welcome to my blog "New Yorko Times" https://yorko.github.io. This blog is about machine learning, mathematics, quantum computation, career development, programming, soft skills, popular science, and anything else I find exciting and (hopefully) can interestingly describe. My...

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mlcourse.ai: free hands-on dive into practical Machine Learning

In this video, we go through the https://mlcourse.ai self-paced roadmap and discuss how to approach the course in its self-paced mode: articles to read, lectures to watch, and assignments to crack. Course: https://mlcourse.ai GitHub repository:...

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BERT classifier fine-tuning with PyTorch, HuggingFace, and Catalyst. Part 4. Training with Catalyst

The final part of the BERT fine-tuning tutorial https://github.com/Yorko/bert-finetuning-catalyst covers the actual training with Catalyst and GPUs.

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BERT classifier fine-tuning with PyTorch, HuggingFace, and Catalyst. Part 3. HuggingFace BERT model

In the 3rd part of the BERT fine-tuning tutorial https://github.com/Yorko/bert-finetuning-catalyst we try to understand the BERT classifier model by HuggingFace and dig into the code of the transformers library.

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BERT classifier fine-tuning with PyTorch, HuggingFace, and Catalyst. Part 2. Data preparation

In the 2nd part of the BERT fine-tuning tutorial https://github.com/Yorko/bert-finetuning-catalyst we cover data preparation for training, from CSV files to PyTorch DataLoaders

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BERT classifier fine-tuning with PyTorch, HuggingFace, and Catalyst. Part 1. Intro

In the 1st part of the tutorial we describe the reusable BERT fine-tuning pipeline https://github.com/Yorko/bert-finetuning-catalyst I'm sharing the pipeline that I actually use at work (that's not a Kaggle Notebook anymore) which follows a modular approach, is easy to run...

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Firing a cannon at sparrows: BERT vs. logreg

There is a Golden Rule in NLP, at least when it comes to classification tasks: “Always start with a tfidf-logreg baseline”. Elaborating a bit, that’s building a logistic regression model on top of tf-idf (term frequency-inverse document frequency) text representation. This...

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Прямая трансляция пользователя Yury Kashnitsky

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How to jump into Data Science

I'm Yury Kashnitsky leading mlcourse.ai https://mlcourse.ai – an open Machine Learning course by OpenDataScience (ods.ai). In this talk, I'll describe the learning path you need to step in to find your first DS job. Assuming that basic ML is covered (mlcourse.ai, Andrew Ng's...

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mlcourse.ai. Lecture 8. Part 2. Vowpal Wabbit

Here we go on with Stochastic Gradient Descent and discuss Vowpal Wabbit library. During live coding, we train a linear model with several gigabytes of StackOverflow questions in just 30 seconds. Accompanying Jupyter notebook (messy, as is) - https://bit.ly/2Q1Ss1z Main site...

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mlcourse.ai Fall 2019 Live Session 0

Here we discuss the course roadmap, activities, what's new, some cool stories (maybe) etc. Slides https://www.slideshare.net/festline/mlcourseai-fall2019-live-session-0

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mlcourse.ai. Lecture -1. Outroduction

Some directions to choose when you have covered basics of Machine Learning: - deep learning (cs231n) - theory of ML - more practice, Kaggle - first job in Data Science - pet projects Some words on the fall 2018 session of mlcourse.ai (thanks to Kirill Vlasoff and Andrew...

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Topic 10. Part 2. Key ideas behind Xgboost, LightGBM, and CatBoost. Practice with LightGBM

In this part, we discuss key difference between Xgboost, LightGBM, and CatBoost. Practice with logit, RF, and LightGBM - https://www.kaggle.com/kashnitsky/topic-10-practice-with-logit-rf-and-lightgbm Main site - https://mlcourse.ai Kaggle Dataset -...

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Topic 10. Part 1. Gradient boosting basics

In this video, we cover fundamental ideas behind gradient boosting, the versatile high-performing machine learning algorithm. Main site - https://mlcourse.ai Kaggle Dataset - https://www.kaggle.com/kashnitsky/mlcourse GitHub repo - https://github.com/Yorko/mlcourse.ai

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Topic 9. Time series analysis

Here we discuss foundations of the ARIMA forecasting model, which is accurate, useful for small time series prediction as well as it's important for understanding time series. We also briefly discuss the Facebook Prophet approach which is more suitable for scalable...

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