Amazon has what may very well be the final robot vacuum sale of 2021. The online retailer has solid deals on three Ecovacs robot vacuums. These deals won't arrive before Christmas, but they should get there before the New Year. The deals end just before midnight on Thursday evening, Pacific time. First up, we have the Deebot Ozmo N7 robot vacuum and mop for $280. This robovac features laser navigation, Lidar-assisted object avoidance, multi-floor mapping, no-go and no-mop zone designations, and 2,300Pa suction.

Finalist for the Los Angeles Times Book Prize A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us―and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge.

Gartner predicts that by the end of 2024, 75% of enterprises will shift from piloting to operationalizing artificial intelligence (AI), and the vast majority of workloads will end up in the cloud in the long run. For some enterprises that plan to migrate to the cloud, the complexity, magnitude, and length of migrations may be daunting. The speed of different teams and their appetites for new tooling can vary dramatically. An enterprise's data science team may be hungry for adopting the latest cloud technology, while the application development team is focused on running their web applications on premises. Even with a multi-year cloud migration plan, some of the product releases must be built on the cloud in order to meet the enterprise's business outcomes.

Until recently, customers who wanted to use a deep learning (DL) framework with Amazon SageMaker Processing faced increased complexity compared to those using scikit-learn or Apache Spark. This post shows you how SageMaker Processing has simplified running machine learning (ML) preprocessing and postprocessing tasks with popular frameworks such as PyTorch, TensorFlow, Hugging Face, MXNet, and XGBoost. Training an ML model takes many steps. One of them, data preparation, is paramount to creating an accurate ML model. Likewise, you often need to run postprocessing jobs (for example, filtering or collating) and model evaluation jobs (scoring models against different test sets) as part of your ML model development lifecycle.

This book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In order for today's organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, today's data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. Despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing HR Analytics functions.

Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Improved techniques and new methods around parsing and processing text are discussed as well. There is also a chapter dedicated to semantic analysis where you'll see how to build your own named entity recognition (NER) system from scratch.

"Please note that the official seller in India is Pothi. Other sellers are selling printouts/photocopies for cheaper prices" This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option.

"Machine learning is ubiquitous at Amazon today," said Rajeev Rastogi, Vice President, Machine Learning at Amazon India, in an interview with Gadgets 360. "Within the retail business, we are using machine learning extensively to recommend products to customers, forecast future demand for products, and improve the quality of a product catalogue, both classifying products, and also eliminating duplicate products." One of the most basic examples of how Amazon is using machine learning (ML) is when you misspell a query on its search bar. The e-commerce site, Rastogi noted, looks at the phonetic distance between the typed misspelt query and the correct query instead of looking at their textual distance to provide accurate results -- no matter whether you have spelt something incorrect. For instance, if you type "geezer" on Amazon to look for the available geyser options, the marketplace will autocorrect the spellings and show you relevant results.

These days there is a wide choice of platforms, technologies, and programming languages for deep learning. In this book all the examples are in Python and most of the code relies on the excellent Keras framework. The example code is available on GitHub as a set of Python notebooks, one per chapter. Python -- Python 3 is preferred, but Python 2.7 should also work. We use a variety of helper libraries that all can easily be installed using pip.