Data Science Digest (July 2019)

Greetings to all!

Summer is in full swing, and if you plan to be in Odessa on July 5th, I invite you to the ODS mitap and data bar , which is organized by the Odessa ODS.ai team. I remind you that the digest has its own Telegram channel and pages in social networks ( Facebook , Twitter , LinkedIn , Medium ), where I publish daily links to useful materials. Join now!
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For now I offer a fresh selection of materials under the cut.

Articles

• 18 Impressive Applications of Generative Adversarial Networks - an overview of 18 interesting GAN applications that will help you understand where it can be used and useful.
• Hardware acceleration of deep neural networks: GPU, FPGA, ASIC, TPU, VPU, IPU, DPU, NPU, RPU, NNP and other letters.
• Time Series Forecasting with TensorFlow.js - In this article, you will learn how to extract stock prices from the online API and make predictions using a recurrent neural network and long-term short-term memory (LSTM) and TensorFlow.js.
• Initializing neural networks - the article explains how to effectively initialize the parameters of a neural network in order to speed up its learning and avoid common mistakes.
• Frontier
• The Third Wave Data Scientist - what a modern data scientist should know and be able to do.
• 16 OpenCV Functions to Start Your Computer Vision journey (with Python code) is an excellent article for beginners, which contains a description of the main functions of the OpenCV library and allows you to quickly start working with it.
• Whether the bubble of machine learning burst, or the beginning of a new dawn .
• A good overview article that contains links to useful resources on natural language processing and helps you learn about the latest trends in this area.
• Distributed Deep Learning Pipelines with PySpark and Keras
• Text Preprocessing in Python: Steps, Tools, and Examples - in this article you will learn about the main stages of text preprocessing, which are necessary for translating text from a human language into a machine-readable format for further work with it.
• Rekko Challenge - how to take 2nd place in the competition for the creation of recommender systems .
• Introducing TensorFlow Graphics - a review of the new addition to TensorFlow, which is expected to allow research at the interface of deep learning and computer graphics.
• Automatic task assignment in Jira using ML .
• A Hands-On Introduction to Deep Q-Learning using OpenAI Gym in Python - this article will help you take the first steps into the world of deep learning with reinforcement using the example of using OpenAI Gym.

Cheatsheets

• Data Science Cheatsheets is an excellent collection of cheat sheets on the following topics: Artificial Intelligence, Big Data Analytics, Big Data, Data Engineering, Data Mining, Data Science, Data Visualization, Deep Learning, Machine Learning, Python, and others.
• Artificial Intelligence cheatsheets for Stanford's CS 221 - this repository summarizes all the important things described in the Stanford course on artificial intelligence CS 221, and includes cheat sheets on it.

Projects

• ICLR 2019 posters is a project that is dedicated to collecting posters from leading machine learning conferences in one place. The first to be added are posters with ICLR.
• Machine Learning and Data Science Applications in Industry is a repository that contains a large list of examples of machine learning and data science in various industries.

Video

• Deep Learning Boot Camp - video presentations from the Deep Learning Boot Camp, which was held from May 28 to 31 in Berkeley.

Books

• AUTOML: METHODS, SYSTEMS, CHALLENGES - this book presents the first comprehensive review of general automatic machine learning methods (AutoML), collects descriptions of existing systems based on these methods, and discusses the problems of AutoML systems.

activity

• ODS.ai Odessa Meetup & Data Bar - July 5, Odessa - the first meeting of the Open Data Science community in Odessa. Informal communication and interesting topics on the eve of the EECVC conference. Participation is free, registration is required.
• 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 first topics of reports are already on the site. For readers of the digest 7% promotional code: DSDigest-AI2019.

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.

← Previous release: Data Science Digest (May 2019)