Python's popularity is growing tremendously and it's becoming more and more relevant economically and technologically. In this 7 in 1 version you get a full collection of The Python Bible series. From the first volume on, you will be lead on a structured way to the mastery of Python. Besides the basics and the intermediate concepts, you will also learn how to apply it in areas like machine learning, financial analysis and neural networks. At the end you will additionally be introduced to one of the most interesting fields of computer science, which is computer vision After reading this collection, you will not only understand the programming language but you will also be able to work on projects in the stated fields.

From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders. Here's what Cassie Kozyrkov, Chief Decision Scientist at Google tells about the book in the Foreword: "You're looking at one of the few true Applied Machine Learning books out there. That's right, you found one! A real applied needle in the haystack of research-oriented stuff. Excellent job, dear reader... unless what you were actually looking for is a book to help you learn the skills to design general-purpose algorithms, in which case I hope the author won't be too upset with me for telling you to flee now and go pick up pretty much any other machine learning book. The machine learning equivalent of a bumper guide to innovating in recipes to make food at scale. Since you haven't read the book yet, I'll put it in culinary terms: you'll need to figure out what's worth cooking / what the objectives are (decision-making and product management), understand the suppliers and the customers (domain expertise and business acumen), how to process ingredients at scale (data engineering and analysis), how to try many different ingredient-appliance combinations quickly to generate potential recipes (prototype phase ML engineering), how to check that the quality of the recipe is good enough to serve (statistics), how to turn a potential recipe into millions of dishes served efficiently (production phase ML engineering), and how to ensure that your dishes stay top-notch even if the delivery truck brings you a ton of potatoes instead of the rice you ordered (reliability engineering). This book is one of the few to offer perspectives on each step of the end-to-end process."

Organizations in virtually every industry are now awash in more data than they know what to do with. But how can they take all of that information and use it to arrive at new insights that help to improve operations and chart a path forward? The exact journey from data, to insight, to decision-making will look slightly different for every organization. But my observations of best practices across industries reveal a common architecture to the process. Anyone who opens up an e-commerce shop on a platform like Shopify almost instantly begins to collect data – information about transactions from different channels, suppliers, inventory, customer reviews, and other sources.

This year is more challenging than most to get presents for your loved ones on time. Fortunately, even if you've waited till the last minute to start shopping, you still have some options. We've gathered up some of the best deals we can find that also have a solid chance of making it to your home before Christmas. Special offer for Gear readers: Get a 1-year subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like). Subscriptions help fund the work we do every day.

Computer monitors that support HDMI 2.1, the latest HDMI standard, are beginning to trickle into online retailers. They sell at extremely high prices (when they're available at all). Even the most affordable HDMI 2.1 monitors, like the Gigabyte Aorus FI32U and Acer Nitro XV282K KV, are priced near $1,000. The high price of HDMI 2.1 implies it's important, but the truth is more nuanced. HDMI 2.1 brings new features to the table, but they're relevant only to people with specific needs.

After forming a partnership with Walmart in July to reoutfit the retailer's distribution network with a fleet of fully autonomous robots, Symbotic has announced plans to become a publicly traded company early next year. Yesterday, the robotics and automation firm announced it will go public via a special acquisition company (SPAC), courtesy of a merger with SoftBank Investment Advisers' SVF Investment Corp 3 (SVFC). Once the merger is finalised in the first half of 2022, the combined company will operate under the name Symbotic and trade on the Nasdaq under the ticker symbol SYM. Both Walmart and Symbotic declined comment following a series of phone calls and emails from Capital.com. In the company release issued on Tuesday, Symbotic chair and CEO Rick Cohen said, "Now is the time to take Symbotic to the next level."

Natural language processing (NLP) has been a hot topic in the AI field for some time. As current NLP models get larger and larger, data scientists and developers struggle to set up the infrastructure for such growth of model size. For faster training time, distributed training across multiple machines is a natural choice for developers. However, distributed training comes with extra node communication overhead, which negatively impacts the efficiency of model training. This post shows how to pretrain an NLP model (ALBERT) on Amazon SageMaker by using Hugging Face Deep Learning Container (DLC) and transformers library.

NOTICE: To buy the newest edition of this book (2021), please search "Machine Learning Absolute Beginners Third Edition" on Amazon. The product page you are currently viewing is for the 2nd Edition (2017) of this book. Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey, there are some high-level theory and statistical principles to weave through first. But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first.

GAN is a generative ML model that is widely used in advertising, games, entertainment, media, pharmaceuticals, and other industries. You can use it to create fictional characters and scenes, simulate facial aging, change image styles, produce chemical formulas synthetic data, and more. For example, the following images show the effect of picture-to-picture conversion. The following images show the effect of synthesizing scenery based on semantic layout. This post walks you through building your first GAN model using Amazon SageMaker. This is a journey of learning GAN from the perspective of practical engineering experiences, as well as opening a new AI/ML domain of generative models.

AutoML is a powerful capability, provided by Amazon SageMaker Autopilot, that allows non-experts to create machine learning (ML) models to invoke in their applications. The problem that we want to solve arises when, due to governance constraints, Amazon SageMaker resources can't be deployed in the same AWS account where they are used. This post walks through an implementation using the SageMaker Python SDK. It's divided into two sections: The solution described in this post is provided in the Jupyter notebook available in this GitHub repository. For a full explanation of Autopilot, you can refer to the examples available in GitHub, particularly Top Candidates Customer Churn Prediction with Amazon SageMaker Autopilot and Batch Transform (Python SDK).