Data Science Digest (April 2019)

Habr, hello!

In March I restored the publication on Habré of the digest devoted to ML and Data Science. Today I prepared a fresh selection of interesting links, and I also announce the launch of the Telegram digest channel , in which I publish links to interesting materials related to AI & ML every day. I invite everyone to join him. In the meantime, I offer a fresh selection of materials under the cut.

Articles

• Forecasting at Uber: An Introduction - This is the first in a series to explain how Uber uses forecasting to create better products and services.
• How to become a Data Engineer .
• Structural Time Series Modeling in TensorFlow Probability is about tfp.sts, a new library in TensorFlow Probability for time series forecasting using structural time series models.
• How to Version Control Jupyter Notebooks - An overview of the various ways to control version of Jupyter Notebooks, including embedded solutions and external tools.
• Why and how do we hide car license plate numbers in Avito ads .
• Computer Vision Tutorial: A Step-by-Step Introduction to Image Segmentation Techniques - A step-by-step guide to introducing image segmentation.
• Jupyter Lab: Evolution of the Jupyter Notebook - Review of JupyterLab, the next generation of Jupyter laptops.
• Hands-on TensorFlow Tutorial: Train ResNet-50 from Scratch Using the ImageNet Dataset is a practical guide to learning the ResNet model in TensorFlow. From launching TensorFlow, downloading and preparing ImageNet, to documenting and reporting.
• How to Choose the Right Chart Type - an infographic that shows the possible types of charts that you can use depending on the data you have.
• Tutorial: Poisson Regression in R - A guide on Poisson regression, what it is and how R programmers can use it in real-world applications.
• GANSynth: Making music with GANs - An introduction to GANSynth, a method for generating high-quality sound using Generative Adversarial Networks (GAN).
• Frameworks for Machine Learning Model Management - A comparison of three popular tools for managing the life cycle of machine learning models / projects: MLFlow, DVC, and Sacred.

Project

• Pandaral·lel is a simple and effective tool for parallelizing your Pandas operations on all available processors.

Datasets

• Mathematics dataset
• 130 Terabytes of Oil and Gas Data

Books

• Dive into Deep Learning is an interactive deep learning book with code, maths and exciting discussions.
• 15 best books on deep learning .

Video

• Scaled Machine Learning Conference 2019 .
• The theory of games around us is an excellent lecture on the theory of games, its application in everyday life and how not to lose.

activity

• Eastern European Conference on Computer Vision - July 6-7, Odessa.
• Lviv Data Science Summer School - July 22 - August 2, Lviv. Registration is open until May 1. The school program has already announced 12 courses in the areas of: Computer Vision, Natural Language Processing, Healthcare, Social Network Analysis, Urban Data Science and others.

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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. You can send your links here .

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