Otros libros que te pueden interesar

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  Living with Epilepsy - An...

  Edmonton Epilepsy Association

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  This audiobook is designed to provide general information about Epilepsy to the public. It does not include specific medical advice. People with epilepsy should not make changes based on this information. Always consult your physician prior to making any changes. 

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  BLOCKCHAIN BASICS BIBLE:...

  Nakamoto Satoshy

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  Do you want to invest, but it seems so complicated? 
  What if there was an easy way? 
  Keep reading to discover everything you need about Blockchain and how to mine to make high profits. 
  More than 1 Billion Dollars have been invested into Blockchain. Hundreds of startups and financial organizations are working on improving and scaling this technology. So what exactly is it? 
  Millionaires are being made because of this new opening in the market. Do you want to stand aside and watch? Or do you want to seize the opportunity? Inside this book, you will learn :Basics of Blockchain: All the necessary fundamentalsHow Blockchain works and the way it doesApplications of Blockchain and how to useWhat makes Blockchain so specialHow to Profit from BlockchainInvesting strategies and potential risksFuture of Blockchain:Discover how mining and cryptocurrency Works 
  The good thing is that Blockchain is still in the early stages. Most people think cryptocurrency mining is too complicated, and you need to be tech-savvy to be a miner; this is far from the truth. 
  I'm giving away all the secrets for you to start cashing in on this tremendous opportunity!  
  I'll show you how to mine, and you get to keep everything! 
  Now It's your turn to take action!  
  Scroll Up and Click "Buy Now" to Get Your Guide Today and start scaling Blockchain Technology today!

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  Metaverse & Financial Services

  John Blicq

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  What are the strategic risks and opportunities, that the Metaverse and the rise of Digital Assets, present to their organisations?With the emergence of the Metaverse and thanks to three decades of technological progress, Digital Humans and Digital Twins are quickly on the rise!But just what are these digital assets?Early signals suggests that a physical asset centric industry like Financial Services will be heavily disrupted by Digital Twins at scale in the Metaverse.It will create new forms of customer needs; new growth corridors and new competitors will rise as the value will shift toward a digital asset centric world.In such a world, Financial Services incumbents will need to adapt quickly to remain relevant.This book introduces the concepts of Metaverse, Digital Twins, Digital Humans, and their foreseeable impacts on the Financial Services value chain.It is destined to inform key decision makers of this industry and help them prepare their organization to remain relevant for what will be the next economical era.Are you ready to uncover the secrets behind the future of digital assets? Then grab your copy of Metaverse & Financial Services, today!

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  Selfish Gene The: Book Summary &...

  Briefly Summaries

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  This is a concise summary and analysis of The Selfish Gene, by Richard Dawkins. It is not the original book and is not affiliated with or endorsed by Richard Dawkins. Ideal for those seeking a quick and insightful overview.
  The Selfish Gene explores the concept that evolution operates at the level of genes rather than individuals or species. It presents the idea that organisms are vehicles for the propagation of their genetic material, with natural selection favoring genes that enhance their chances of survival and replication. The book delves into the role of behavior, cooperation, and altruism, offering a new perspective on evolution that emphasizes the selfish nature of genes in shaping life.

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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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  Python Data Analysis for...

  Brian Murray

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  Python Data Analysis for Beginners: A Beginner's Handbook to Exploring and Visualizing Data is a comprehensive guide designed to help readers master the fundamentals of data analysis using the Python programming language. Whether you're a beginner looking to get started with data analysis, or a professional looking to expand your skill set, this book provides a step-by-step approach to exploring and visualizing data with Python. 
  The book starts with an introduction to Python programming language and its essential libraries for data analysis. Readers will learn how to manipulate and transform data using Pandas library, and how to create visualizations using Matplotlib and Seaborn libraries. The book also covers exploratory data analysis (EDA) techniques using Python, including data preprocessing and visualization techniques. 
  In addition, the book introduces readers to machine learning concepts and provides an overview of supervised and unsupervised learning using Python. The book includes several case studies and real-world applications of data analysis using Python, including analyzing and visualizing social media data, financial data, and healthcare data. 
  Written in a clear and concise language, Python Data Analysis for Beginners provides readers with a solid foundation in data analysis using Python. The book is suitable for self-learners, as well as instructors teaching data analysis to students. With its step-by-step approach and real-world examples, this book is an essential resource for anyone looking to master data analysis with Python. 
   

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