Data Science Theory

The deep-learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep-learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

Fuelled by a surge of migration to cities, the country's growth appears to be defined by urbanisation and by its growing, prosperous middle class. It is also defined by progressive and liberal young Indians, who vote beyond the constraints of identity, and paradoxically, by an unchecked population explosion and rising crimes against women. Is it, though?

We are in the 4th industrial revolution; companies need to figure out how to survive. In this exciting revolution, machine intelligence has had a more unprecedented impact on business than the internet, and it's the only path to corporate survival in the future... Leaders don't have to be scientists to unlock the power of AI technology that is already radically altering the industrial landscape. If you're ready to meet the challenges of this new revolution, this essential guide will help you take your business to the next level.

Whether you're new to the world of programming and are looking for clarity on what direction to take, or you're simply curious about what machine learning, AI, and big data has in store for the future and want to prepare for it, this guide makes it easy to actually understand implement machine learning and data analytics effectively.

In our highly competitive world of today, we need all the edge we can to get ahead of our competitors. For a long time, companies have focused on spending more on marketing and advertisement, ignoring the powerful asset they had at their disposal: data. Although this isn't the case anymore for large companies, smaller businesses are still in the dark about this, and you can use this asset to your advantage. This bundle will show you how.

Models are at the centre of everything we do. They act as metaphors that help us better understand the increasingly complex problems facing us in the modern world. Yet in recent years, the validity of the models we use has been hotly debated and there has been renewed awareness of the disastrous consequences when the makers and interpreters of models get things wrong. Drawing on contemporary examples from finance, climate and health policy, Erica Thompson explores what models are, why we need them, how they work and what happens when they go wrong.

Data Science 2020 can help you go from novice to expert in no time at all. To keep up with this growing industry, one has to know all the ins and outs, be able to keep up with the fast-moving innovations, and really understand all the nuances of this field. If you’ve ever wanted to get a leg up in this competitive field, make yourself more marketable or vital to businesses, or have a vested interest in learning all there is to know about data science, this comprehensive guide will walk you through the process from beginning to end.

We live in an incredible period in history. The computer revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before. But our love of computers should not cloud our thinking about their limitations. The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

In Fusion Strategy, world-renowned innovation guru Vijay Govindarajan and digital strategy expert Venkat Venkataraman offer a first-of-its-kind playbook that will help industrial companies combine what they do best-create physical products-with what digitals do best-use algorithms and AI to parse expansive, interconnected datasets-to make strategic connections that would otherwise be impossible.

In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally - hiring, driving, paying bills, even choosing romantic partners - that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology.

Decision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies. But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data. With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap.

In this accessible and comprehensive audiobook, we start from scratch, assuming no prior knowledge. Each chapter is carefully crafted to guide you through the fundamental concepts and practical applications of AI, including machine learning, generative AI, and deep learning. With clear and plain English explanations, you'll be surprised at how quickly you can start thinking and speaking fluently in the words and mindset of an AI expert!

A team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.

Today, 91 percent of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey. Do you know why? The reason is that they have no notions of artificial intelligence. So, future programmers will no longer find work without having knowledge of this particular field that is constantly evolving.

If you need to learn how to use machine learning, big data, data science, and neural networks but you can’t (or there is no time to) study the complicated math and algorithms behind these technologies, then keep reading. All technologies related to artificial intelligence represent an incredible opportunity if you want to grow your business or if you are searching for a new job, but it’s not very easy to understand how they work.

Data Lake Architecture will explain how to build a useful data lake where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess.

Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service within the Microsoft Azure cloud computing platform.

Welcome to the exciting world of AI-powered trading bots for executing options trades. In this subchapter, we will explore the fundamentals of AI and option trading, providing you with a solid foundation to begin your journey into the world of trading stocks and options. Whether you are a novice trader looking to learn the basics or an experienced investor seeking to leverage the power of AI technology in your trading strategies, this subchapter is designed to help you understand the key concepts and principles that drive success in the world of option trading.

Un científico de datos es alguien que sabe más de informática que un estadístico, y conoce más estadísticas que un científico de la computación. Si bien, en gran medida, esto es cierto, convertirse en un buen científico de datos requiere el dominio no solo de estas dos áreas clave, sino también de algunas teorías y modelos cruciales para este campo. El libro se recomienda a todos los lectores interesados ​​que aspiran a destacarse en el campo de la ciencia de datos.

Artificial intelligence is the secret behind the big ones like Google, Facebook, Amazon, and we all know that. But it can also be a powerful tool in your own hands. With this audiobook, you will prepare the ground for your future success, either if you want to start up your own AI enterprise, apply your knowledge to your current business, or find a job at the greatest and most innovative companies. If programming will open you many doors, Python programming will open you even more.