Data science digest

Habr, hello!

For a long time I conducted a digest on Habr dedicated to AI and BigData. Now I want to restore it and once a month make a selection of interesting materials from the field of Data Science.

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 already done so. 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 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  - about developing an ML model for predicting breast cancer using histological images.
• Overview of the TOP Algorithms for Machine Learning. Part 1 - in the first part, various types of machine learning are considered, as well as algorithms such as linear regression, K-Nearest Neighbors (kNN) and a convolutional neural network. Python examples are included.
• Building fully custom machine learning models on 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 using Keras and predicting housing prices based on a set of images.
• What Kagglers are using for Text Classification - an overview of models for classifying text with examples of their use.
• AlphaStar: Mastering the Real-Time Strategy Game StarCraft II  is about AlphaStar, which won StarCraft II the best professional players.
• Fashion MNIST with Keras and Deep Learning  is about creating a convolutional neural network for classifying clothes using the Fashion MNIST dataset.
• A comparison of different GPUs in terms of performance and price for Deep Learning can be found in Tim Dettmers, which he constantly updates. And if you need Multi-GPU servers for work , then 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 related code.
• Recommenders  is an open-source project from Microsoft that collects best practices for building recommendation 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

• On March 16, the 6th annual Data Science UA conference will be held in Kiev . The program on the site, discount 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 for the digest groups in social networks ( Facebook , Twitter , Telegram ) and so that, without waiting for the release date of the digest, I immediately publish links to interesting materials in them.

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

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