Data Science Digest

Habr, hello!

A long time ago I was leading on Habré a digest dedicated to AI and BigData. Now I want to restore it and once a month to make a selection of interesting materials from the field of Data Science.
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In January, I began a survey of data scientists about their work tools. During this time, more than 600 people have already taken part in it. Until March 3, you can still take part in it , if you have not done it yet. I plan to publish the results in the 10th of March, but for now I offer a fresh selection of materials under the cut.

Articles

• Scaling Machine Learning Productivity at LinkedIn - on LinkedIn 's approach to scaling ML systems and sharing of knowledge among engineers.
• Image Segmentation using Python's scikit-image module - an overview of image segmentation methods using scikit-image.
• Breast cancer classification with Keras and Deep Learning is about developing an ML model for predicting breast cancer using histological images.
• Overview of the TOP Algorithms for Machine Learning. Part 1 - the first part deals with different types of machine learning, as well as such algorithms as linear regression, K-Nearest Neighbors (kNN) and convolutional neural network. Examples in Python are attached.
• Building AWS SageMaker - a practical guide to working with AWS SageMaker.
• Keras, Regression, and CNNs - on the development of a convolutional neural network for predicting regression with Keras and predicting housing prices based on a set of images.
• What Kagglers are using for Text Classification - an overview of models for text classification with examples of their use.
• AlphaStar: Mastering the StarCraft II Real-Time Strategy Game - about AlphaStar, which won the best professional players in StarCraft II.
• Fashion MNIST with Keras and Deep Learning - about creating a convolutional neural network for classifying clothes using Fashion MNIST dataset.
• A comparison of different GPUs in terms of performance and price for Deep Learning can be found in the Tim Dettmers material, which he constantly updates. And if you need a Multi-GPU server for your work, I will be happy to help with this. You can write to me in PM.
• An overview of the NLP ecosystem in R - Mindmap map of the NLP ecosystem in R.
• Framework for Better Deep Learning .
• Predicting Irish electricity consumption with neural networks in R and Python - about creating a neural network to predict energy consumption in R and Python.

Projects

• Papers with Code is a project that contains links to machine learning articles along with the corresponding code.
• Recommenders is an open-source project from Microsoft, which brings together best practices for building recommender systems.

Books

• MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence by Phil Kim
• Great Books for Data Science
• 13 Classic Mathematics Books for Lifelong Learners

Video

• Deep Learning UC Berkeley STAT-157 2019

activity

• March 16 in Kiev will be held the 6th annual conference of Data Science UA . The program on the site, promotional code - DataScienceDigest.
• List of Machine Learning / Deep Learning conferences in 2019

I have an English version of this digest, which you can subscribe to here . I also created groups for social networks ( Facebook , Twitter , Telegram ) for the digest and, without waiting for the release date of the digest, immediately publish in them links to interesting materials.

If you are from Odessa, join our group at FB or meetup.com .

Thank you for reading this release. I hope everyone found something useful for themselves. I would be grateful for any suggestions for the next digest.