Data Science Digest (May 2019)

Habr, hello!

In the last issue I told you that I launched the Telegram channel for digest, and today I want to share the news that I also started Facebook , twitter , LinkedIn pages for it. I invite everyone to join them.
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In addition, today we published a digest on Product Hunt , who knows what it is - those who know what needs to be done;)

For now I offer a fresh selection of materials under the cut.

Articles

• Benchmarking Edge Computing - comparing single-board computers for ML, such as the Coral Dev Board, NVIDIA Jetson Nano, the Coral USB Accelerator, the Movidus Neural Compute Stick and the Intel Neural Compute Stick 2.
• The Google Coral Edge TPU Board Vs NVIDIA Jetson Nano Dev board is another comparison of two single-board computers.
• Getting started with the NVIDIA Jetson Nano - a detailed guide to start working with NVIDIA Jetson Nano: from the first launch to using it for classification and detection of objects.
• Google Coral USB Accelerator object detection and image classification - an article on how to use the Google Coral USB Accelerator to classify and detect objects in images or videos.
• Random Forests for Complete Beginners is a good article for beginners about random forests and decision trees.
• Introducing d3-regression - basics of working with d3-regression - D3.js module for calculating statistical regressions from two-dimensional data.
• Analysis and Forecasting with Python on how to use time series for non-stationary data, such as economic data, weather, stock prices and retail sales. Different approaches to forecasting retail sales using time series are considered.
• Using Reinforcement Learning to Design a Better Rocket Engine - how to use reinforcement learning in rocket engine design.
• Top 5 Interesting Applications for GHGs for Every Machine Learning Enthusiast - an overview of five popular GAN uses that are common in the industry: GAN for image editing, using GAN for security, generating data using GAN, GAN for predicting attention, GAN for generating 3D -objects.
• A Recommendation Model with PyTorch - what is probabilistic matrix factorization and how it can be used for recommender systems.
• Easy Image Classification with TensorFlow 2.0 - familiarity with TensorFlow 2.0 and its use in the classical classification of images.
• Bayes Theorem in One Picture
• Advanced Keras - Constructing Complex Custom Losses and Metrics - on how to create your own loss functions in Keras, which can take arguments other than y_true and y_pred.
• Best of arXiv.org for AI, Machine Learning, and Deep Learning - March 2019 - the best arXiv.org articles for March according to the version of the website insideBIGDATA.

Project

• Fast Neural Style Transfer in PyTorch
• CariGANs: Unpaired Photo-to-Caricature Translation

Datasets

• MRNet Dataset
• A Repository of Conversational Datasets

Books

• Interactive web-based data visualization with plotly, and shiny
• Forecasting: Principles and Practice

Conferences

• Deep Learning Summit London - September 19-20, 2019 (Promo code for a 20% discount - DSD)

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.

← Previous release: Data Science Digest (April 2019)