Otros libros que te pueden interesar

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  Navigating the Machine Learning...

  Aria Chen

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  Unlock the secrets of machine learning with this comprehensive guide! Explore the complex world of data models, algorithms, and practical applications. Learn how machines can be programmed to learn, adapt, and predict, and how you may use these technologies to solve problems creatively and make wise decisions. 
  "Navigating the Machine Learning Landscape: A Primer to Algorithms, Data Models, and Applications" takes readers on a journey through the fundamental ideas, innovative techniques, and real-world applications of machine learning, thereby capturing the revolutionary power of this field. It is painstakingly designed for professionals, innovators, and enthusiasts who are keen to learn about and become experts in the ever-evolving field of machine learning. Every chapter reveals new information at every level, from fundamental ideas to intricate algorithms, guaranteeing readers of all skill levels a thorough comprehension. 
  Imagine having the ability to build intelligent systems that can forecast, adapt, and learn. With the help of this book, you'll be able to turn your ideas into reality. Explore a variety of data models, unravel intricate algorithms, and comprehend the moral and practical implications of machine learning. Each section is enriched with real-world examples, case studies, and applications that breathe life into theoretical concepts, ensuring you're not just informed but also equipped to implement, innovate, and lead in this field. 
  Take advantage of this chance to improve your machine learning knowledge and proficiency. Take a voyage of exploration, creativity, and expertise into the fascinating field of machine learning. Change not only what you know, but also your ability to lead, innovate, and solve problems in the digital age. 
  Your expedition into the future of technology, innovation, and intelligent systems begins here and now!

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  Data Analysis for Beginners -...

  Daniel Garfield

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  In a data-driven world, the ability to analyze and derive insights from vast amounts of information is becoming increasingly crucial. If you're new to the field of data analysis and eager to dive into this fascinating realm, "Data Analysis for Beginners" is the perfect companion on your learning journey. 
  This comprehensive guide equips you with the essential knowledge and practical skills needed to navigate the complex world of data analysis. Starting from the basics, you'll learn how to effectively clean, manipulate, and explore datasets, ensuring data accuracy and reliability. Through hands-on examples and real-world scenarios, you'll gain a solid foundation in data wrangling techniques and uncover hidden patterns and trends. 
  As you progress, you'll discover the art of data visualization, transforming raw data into captivating visual representations. From creating informative charts and graphs to designing interactive dashboards, you'll learn how to communicate complex insights in a visually compelling and understandable way. With the help of popular tools and libraries like Python, R, and Tableau, you'll master the art of data storytelling and make your analyses more impactful. 
  But data analysis is more than just visual representation. This book also guides you through the realm of statistical analysis, helping you understand the underlying principles and techniques. You'll learn about probability, hypothesis testing, and correlation analysis, empowering you to draw meaningful conclusions and make informed decisions based on data. 
  "Data Analysis for Beginners" is your go-to resource for mastering the art of data analysis. Join the ranks of data-savvy individuals and unleash the power of data to drive meaningful change and gain a competitive edge in today's data-driven world. 
   

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  Machine Learning with Python -...

  Alexander Cane

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  Ways to combat retraining depend on the algorithm and consist of the correct values of the trainer's met parameters. In practice, model estimation is not performed on the same input data that was used to train the model. Divide 10-20% of all available data into a separate set and call it a set for evaluation. We will bring the other 10-20% into the set for ratification, and 60-80% of the remaining ones will be given to the trainer. The principle of data sharing depends on the data and the task. Random sampling is often a good method if inputs are independent of each other, and there is no strong imbalance between the number of positive and negative entries. 
  The intuitive analogy here is the same as with university studies: the teacher solves some problems with students in pairs and gives other’s similar tasks in the exam. What is important here (both in teaching students and models) is that these tasks are varied, and students cannot simply memorize the answers, and those who have mastered the material (similar tasks) will be able to repeat the thought process and answer correctly. 
  In machine learning, we split into data two sets: we will use the evaluation set to evaluate each model we train, using different approaches, algorithms, and model types to select the best one. That is, for each model, we will have two precision values - precision on the training dataset and precision on the evaluation dataset. It is normal for the former to be higher than the second, but not significantly. A big difference indicates retraining.

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  The Effluent Society

  Norman Thelwell

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  Artist Norman Thelwell is best known for his cartoons depicting plump, petulant ponies and their young riders, but The Effluent Society shows another aspect of his work and character. Thelwell was passionate about the countryside and nature. During his life he became increasingly concerned about the impact of development, pollution and society's treatment of the environment. When The Effluent Society was first published in 1971, his concerns were way ahead of their time. Today, environmental and conservation issues are in the mainstream and have become a global priority.
  
  Although the cartoons focus on a serious topic there is no mistaking Thelwell's inimitable style. He used his talent and humour to convey a powerful message: the need to protect the environment. He also takes a wry look at modern life and so-called progress. Thelwell admitted that out of all his books, The Effluent Society was the one that gave him the greatest 'personal satisfaction' and, as such, the re-issue of this title is a fitting tribute to the artist.

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  How to Create Outstanding...

  Vines Graener

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  People form their first impression of you—and your business—in under a second. It’s a startling thought. Fortunately, online articles have a bit more leeway. Most web pages have just under a minute to capture visitors' attention and persuade them to stay or leave. 
  That said, sixty seconds isn’t much time. If you need to convince a new user that your content is worth their clicks and shares, you might think brevity is key. Surprisingly, that’s not always the case. To truly stand out in a crowded digital landscape and keep users engaged, long-form articles are the way to go.

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  Not with a Bug But With a...

  Ram Shankar Siva Kumar, Hyrum...

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  A robust and engaging account of the single greatest threat faced by AI and ML systems. 
   
   
   
  In Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What to Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats. The authors take you on a sweeping tour—from inside secretive government organizations to academic workshops at ski chalets to Google's cafeteria—recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes. 
   
   
   
  Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.

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