Data Science Digest (August 2019)

Greetings to all! DataFest returns to Ukraine and will be held on September 7 in Odessa. Now a program is being formed, but if you want to make a presentation, you can submit your topic here . Register as a member here . I remind you that the digest has its own Telegram channel and pages on social networks ( Facebook , Twitter , LinkedIn ), where I publish links to useful materials daily. Join now!



In the meantime, I offer a fresh selection of materials under the cut.

Articles

• Personalized Recommendations for Experiences Using Deep Learning - in this article you will learn how the recently recommended TripAdvisor's Recommended For You (RFY) model generates personalized recommendations on a website using browsing history and deep learning.
• Building Data Pipelines With Kafka - This article is intended for novice engineers who are going to build their first Kafka-based data pipeline.
• MIT Deep Learning Basics: Introduction and Overview with TensorFlow - An overview of 7 deep learning architectures with links to TensorFlow examples for each.
• Introducing Dagster - an introduction to Dagster - a Python library for building date applications based on ETL processes or ML pipelines. Link to itHub .
• Using DVC to create an efficient version control system for data projects - in this article you will learn what DVC is and how to use it to version a project.
• Top 10 Statistics Mistakes Made by Data Scientists - 10 mistakes that Data Scientists make using statistics, including examples and solutions.
• Anomaly Detection for Dummies - the article considers the detection of uncontrolled anomalies for one-dimensional and multidimensional data, as well as various strategies for their detection. Jupyter notebook.
• Top 25 pandas tricks - 25 tips on how to work faster and write code better using pandas.
• Tips for Training Likelihood Models - in this article you will learn about common methods of training generative models that optimize the probability for autoregressive models and normalizing flows.
• Panel: A high-level app and dashboarding solution for the PyData ecosystem - an introduction to Panel - a new Python library that allows you to create custom interactive web applications and dashboards.
• How to Perform Face Recognition With VGGFace2 in Keras - in this article you will learn about the VGGFace and VGGFace2 models for face recognition; how to develop a face identification system to predict celebrity names from given photographs; how to develop a face verification system to verify a person’s identity from a photograph.
• Neural Architecture Search at CVPR 2019 - In this article you will learn about neural architecture search (NAS) and how the NAS was introduced at CVPR 2019.
• And voilà! - In this article, you will learn how Voilà turns Jupyter notebooks into standalone web applications.
• Building Lyft's Marketing Automation Platform - An article about the Lyft marketing platform, which is powered by machine learning.
• Best Resources for Getting Started With GANs - A selection of good resources for exploring generative adversarial networks: usage methods, video presentations, articles, and books about GAN.

Project

• TRFL is a TensorFlow library that facilitates the implementation of reinforcement learning agents developed by a research team at DeepMind.
• NumPy implementations of various ML models - a repository with various machine learning models based on NumPy.
• PyTorch Image Models - a repository of various ML models on PyTorch for working with images.

Video

• ICML 2019 Videos - videos and slides from the International Conference on Machine Learning.

Books

• Advanced r
• Study E-Book - The repository contains many e-books about computer vision, deep learning, machine learning, math, NLP, Python and reinforcement learning.

activity

• Moscow Data Science Major - August 31, Moscow - a free conference of the Open Data Science community. 11 sections: Summer ML conf, SysML, Fail / cess story, ML trainings, A / B testing, NLP, PyData, ML4SG, CV / Learn to match, DS 4 Life, Random beer. Participation is free, registration is required.
• Odessa Data Fest - September 7, Odessa is a free Open Data Science community conference. 4 sections: Computer Vision, NLP, SysML, Business & Cases. Participation is free, registration is required.
• Data Science fwdays'19 - September 7, Kiev - a conference on AI and Data Science. 2 streams of reports await you; discussion corners; afterparty. Tickets For readers of the digest, a 15% discount on the promotional code: digest_fwdays.
• AI Ukraine 2019 - September 21-22, Kiev - one of the most powerful AI conferences in Ukraine this year will be held in 3 streams: Data Science and Machine Learning; Big Data and Analytics; AI Business and Startups. The program is already on the site. For readers of the digest, a 7% discount on the promotional code: DSDigest-AI2019.
• Data Science UA Conference - October 19, Kiev - 7th Conference on Machine Learning, Artificial Intelligence and Data Science in Kiev. Productive networking and technical insights. Over 500 participants and 20 speakers, 3 streams. More info . For readers of the digest, a 10% discount on the promo code: DSUA_Digest.

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. Join the Telegram channel of the digest and its pages on social networks: Medium , Facebook , Twitter , LinkedIn .



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