Yuli Vasiliev is a programmer, freelance author, and consultant with more than two decades of experience. He began as a developer of database-driven applications, using Oracle database technology. The need for data analysis led him eventually to the field of ML and AI. His present professional interests are in the area of natural language processing (NLP). He runs the @stocknewstip_bot in Telegram, which is available at https://t.me/stocknewstip_bot.

Managing discount promotional events ("markdown") is a significant part of running an e-commerce business, and inefficiencies here can significantly hamper a retailer's profitability. Traditional approaches for tackling this problem rely heavily on price elasticity modelling. However, the partial information nature of price elasticity modelling, together with the non-negotiable responsibility for protecting profitability, mean that machine learning practitioners must often go through great lengths to define strategies for measuring offline model quality. In the face of this, many retailers fall back on rule-based methods, thus forgoing significant gains in profitability that can be captured by machine learning. In this paper, we introduce two novel end-to-end markdown management systems for optimising markdown at different stages of a retailer's journey. The first system, "Ithax", enacts a rational supply-side pricing strategy without demand estimation, and can be usefully deployed as a "cold start" solution to collect markdown data while maintaining revenue control. The second system, "Promotheus", presents a full framework for markdown optimization with price elasticity. We describe in detail the specific modelling and validation procedures that, within our experience, have been crucial to building a system that performs robustly in the real world. Both markdown systems achieve superior profitability compared to decisions made by our experienced operations teams in a controlled online test, with improvements of 86% (Promotheus) and 79% (Ithax) relative to manual strategies. These systems have been deployed to manage markdown at ASOS.com, and both systems can be fruitfully deployed for price optimization across a wide variety of retail e-commerce settings.

He graduated w/Special Honors in ChE & later Cert. in Quality Mgmt. Syndicated research (silicon photonics); writes for trade press and web communities. Served Fortune 1000 and FTSE 250 companies in a variety of projects, including global market/product strategy and most recently deep analytics and forecasting. Following ten years in government research and management (Deputy Director, National Measurement Laboratory (US DoC NIST) and Chief, Chemical Engineering Division of NIST), Mr. Bateman worked at several start-ups in electronics and antennas, resulting in 100s of products and several patents. Mr. Bateman led efforts to bring design and manufacturing of telematics and in-building antennas to China and Malaysia, and was key in creating an Automotive Connectivity Unit in Laird, and led technical diligence for multiple acquisitions and creation of an Infrastructure Antenna Unit.

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You'll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning.

You can find lots of information on Kaggle about competing, but it is difficult to know what is relevant and also very expensive in terms of time and effort – so we put all the essential knowledge into one book. Konrad: My favorite part is Chapter 12 on simulation competitions. Reinforcement learning is a field I have been getting into over the last few years – unlike computer vision or NLP, it has yet to reach wider appeal outside academic circles. It was an interesting and educational experience to try and distill what I have learned into a useful introduction to that fascinating domain. Luca: I enjoyed writing about the history of Kaggle and the professional opportunities it offers.

Kim Kardashian has teamed up with Apple on a custom version of the Beats Fit Pro. The new Beats x Kim collab is available in a trio of skin colors, which are meant to either "blend in or stand out," according to Kardashian. They are still just your standard Beats Fit Pro beneath that skin-tone plastic. But don't expect to find them at all the usual locations when they hit retail on August 17th. In-person sales are limited to 10 Apple stores (including 5th Avenue, Regent Street and Champs-Élysées) as well as several fashion chains and department stores.

The layoffs are part of a restructuring that iRobot said will save the company up to $10 million in 2022 and between $30-$40 million in 2023. This was the same day iRobot announced it was being acquired by Amazon for $1.7 billion. However, iRobot said the two events are not related. To better align costs with near-term revenue, part of the restructuring includes shifting certain non-core engineering functions to lower-cost regions and increasing use of iRobot's joint design manufacturing (JDM) partners. "These actions help support the company's near-term priorities to drive innovation by executing on its product roadmaps, optimize inventory levels across all major channels, expand DTC sales and position the business for profitable growth in 2023," iRobot said in its earnings statement.

Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, omnichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data availability, representing transactions, users, and background product information. Finding products related in different ways, i.e., substitutes and complements is essential for users' recommendations at the vendor's site and for the vendor - to perform efficient assortment optimization. The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm. We also provide its experimental evaluation with regards to the state-of-the-art Shopper algorithm, studying the relevance of recommendations with surveys from industry experts. It is concluded that the new approach presented here offers suitable choices of recommended products, requiring a minimal amount of additional information. The algorithm can be used in various enterprises, effectively identifying substitute and complementary product options.

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