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Building Machine Learning Powered Applications
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to ...
Effective Machine Learning Teams
Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team's flow when building ML systems and products. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common tra...
Machine Learning for Hackers
If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ...
Kubeflow for Machine Learning
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud...
Learning Ray
Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started. Learn how to build your first distributed applications with Ray Core Conduct hyperparameter optimization with Ray Tune Use the Ray RLlib library for reinforcement learning Manage distributed training with the Ray Train library Use Ray to perform data processing with Ray Datasets Learn how work with Ray Clusters and serve models with Ray Serve Build end-to-end machine learning applications with Ray AIR
Helena Rubinstein
Helena Rubinstein was born into a poor Polish family at the end of the nineteenth century; by the time of her death in 1965 she had built a cosmeti empire that spanned the world. When Rubinstein opened her first salon in Melbourne, her scientific approach to beauty was an instant sensation. Women just couldn't get enough of her innovative advice on skincare, and her beauty products were constantly sold out. Having conquered Australia, Rubinstein went on to open salons in Europe and America, at a time when women were barely seen in business, let alone running their own multinational companies. Dressed by Chanel and Yves St Laurent, painted by Salvador Dali and Picasso and mingling with Colette and Proust, Helena Rubinstein not only enjoyed unbelievable success, but was also instrumental in empowering and liberating women. Helena Rubinstein was a total original, and her legacy can still be seen today in the methods used to market and manufacture cosmeti. This is her amazing life story.
AI-Powered Productivity
AI-Powered Productivity is a guide to understanding and using AI and generative tools in professional settings. Chapter 1 introduces AI basics, its impact on various sectors, and an overview of generative AI tools. Chapter 2 delves into large language models exploring their integration with multimodal technologies and effects on productivity. Chapter 3 offers a practical guide to mastering LLM prompting and customization, with tutorials on crafting effective prompts and advanced techniques, including real-world examples of AI applications. Chapter 4 examines how AI can enhance individual productivity, focusing on professional and personal benefits, ethical use, and future trends. Chapter 5 a...
What Should We Do with Our Brain?
Recent neuroscience, in replacing the old model of the brain as a single centralized source of control, has emphasized plasticity,the quality by which our brains develop and change throughout the course of our lives. Our brains exist as historical products, developing in interaction with themselves and with their surroundings.Hence there is a thin line between the organization of the nervous system and the political and social organization that both conditions and is conditioned by human experience. Looking carefully at contemporary neuroscience, it is hard not to notice that the new way of talking about the brain mirrors the management discourse of the neo-liberal capitalist world in which ...
Idiot Engineering
We all, especially software engineers, are on a journey of enhancing Artificial Intelligence (AI). Some of us have even dedicated our lives to it. Every part of the journey is astonishing, including the challenges we encounter. Our struggles sometimes lead to ineffective outcomes, and because of this, some people consider what we are doing, or what we have done, boring or even idiotic. Nevertheless, this journey would not be fulfilling without all the highs and lows. Whether or not we agree with it, AI is here and here to stay. What we need to do is embrace Artificial Intelligence, and we need to enjoy the journey! But how do we enjoy it? This book will help us do that by explaining the definition of AI, the potentials and the risks of AI, how to work on AI, how to utilize AI, and how to increase AI literacy through education!
Machine Learning For Dummies
One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate a...
