Altri libri che potrebbero interessarti

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  The Titanium Economy - How...

  Asutosh Padhi, Gaurav Batra,...

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  The future of the American economy is hiding in an unlikely place: the manufacturing sector While Silicon Valley titans dominate headlines, many of the fastest-growing, most profitable companies in the United States are firms you’ve likely never heard of, such as HEICO, Trex, and Casella. These booming companies belong to a burgeoning sector—industrial tech—that offers surprising hope to workers, consumers, and investors alike.  Their role: to make a range of products—aerospace parts, for example, or recycled plastic lumber—that quietly form the backbone of America’s biggest industries. In an age of instability, industrial tech is a cornerstone of our economic future. In this book, McKinsey veterans Asutosh Padhi, Gaurav Batra, and Nick Santhanam reveal the “titanium economy,” a modern, reinvented industrial sector complete with high-paying, domestic jobs;, soaring stock prices;, and critical infrastructure. They dispel the myth that the best of American manufacturing is behind us and illuminate an opportunity for a brighter future—if we can seize it.

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  Plato's Phaedrus

  Plato

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  Plato's dialogues frequently treat several topics and show their connections to each other. Phaedrus is a model of that skill because of its seamless progression from examples of speeches about the nature of love to mythical visions of human nature and destiny to the essence of beauty and, finally, to a penetrating discussion of speaking and writing. It ends with an examination of the love of wisdom as a dialectical activity in the human mind.Phaedrus lures Socrates outside the walls of Athens, where he seldom goes, by promising to share a new work by his friend and mentor, Lysias, a famous writer of speeches. This dialogue provides a powerful example of the dialectical writing that Plato uses to manifest ideas that are essential to human existence and to living a good life. Phaedrus shows how oral and written forms of language relate to each other and to philosophy. It simultaneously embodies the entire process in some of the greatest poetry ever written.© Agora Publications

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  Lulu Alice and Jimmie Wibblewobble

  Howard R. Garis

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  The Wibblewobble family of ducks appear in a series of 31 bedtime stories - one for each day of the month - written by Howard R. Garis. Each story is an adventure featuring the ducks and their neighbors, including Uncle Wiggily Longears, the famous old gentleman rabbit with rheumatism.  - Summary by Walter Allan

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  Pipefuls

  Christopher Morley

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  A delightful collection of 48 essays on various topics of the human condition that caught his fancy. Witty, insightful and funny of course and on occasion thought provoking and even disturbing. From the preface "These sketches gave me pain to write; they will give the judicious patron pain to read; therefore we are quits. I think, as I look over their slattern paragraphs, of that most tragic hour—it falls about 4 p. m. in the office of an evening newspaper—when the unhappy compiler tries to round up the broodings of the day and still get home in time for supper. And yet perhaps the will-to-live is in them, for are they not a naked exhibit of the antics a man will commit in order to earn a living? In extenuation it may be pleaded that none of them are so long that they may not be mitigated by an accompanying pipe of tobacco." (Summary by phil chenevert)

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  Dropshipping Shopify Success...

  Elisabeth Evers, Vernon Hands

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  As you begin to accrue wealth, having multiple revenue streams becomes increasingly vital. 
  Of course, you can set aside a certain percentage of your monthly salary to invest or save up for a rainy day. But given the current rising inflation rate, many of us find it harder to do so now than ever before. 
  That is, unless you are able to set up various income sources. Thankfully, with rapid technological advancement in recent years, we can now more easily accomplish that. 
  In my book, Dropshipping Shopify Success: Create a Profitable Side Hustle Income With Ecommerce, I will teach you exactly how to earn money and build a nest egg. 
  Dropshipping is quick and easy to set up, and the best part is that you can do it from the comfort of your own home, be it from your home office or even your couch. 
  Moreover, it doesn't require you to maintain a physical shop or handle stock, which makes the setup cost only a fraction of what you might expect. 
  Within its pages, I will explain the concept in much greater detail, breaking down complicated business jargon and providing you with a step-by-step guide on how to start and run your own e-commerce venture from home, where you can make a profit of $2,000 and up per month. 
  It's time to expand your horizons and create a passive income stream that will help you diversify your investments, grow your savings, and acquire things that were previously beyond your reach! 
  Grab your copy of Dropshipping Shopify Success: Create a Profitable Side Hustle Income With Ecommerce today!

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  Machine Learning System Design...

  James Ferry

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  Designing and building machine learning (ML) systems can seem daunting for beginners, but understanding the foundational steps and principles can simplify the process. At its core, ML system design involves a series of well-defined steps that guide the transformation of raw data into valuable insights through predictive models. Here’s a beginner’s guide to understanding and implementing these steps effectively. 
  The first step in designing an ML system is problem definition. Clearly defining the problem you aim to solve is crucial. This involves understanding the business context, identifying the goals, and determining the type of problem—whether it is classification, regression, clustering, or another ML task. A well-defined problem ensures that the subsequent steps are aligned with the desired outcomes. 
  Once the problem is defined, the next step is data collection and preprocessing. Data is the backbone of any ML system, and its quality significantly impacts the performance of the models. Collect data from various sources and ensure it is relevant to the problem. Data preprocessing involves cleaning the data to handle missing values, removing duplicates, and normalizing the data. It also includes feature engineering, which involves selecting, modifying, or creating new features that enhance the predictive power of the model. 
  Finally, the deployment and monitoring phase ensures that the ML model is operational and continues to perform well over time. Deploy the model to a production environment where it can make real-time predictions or be used in batch processing. Implement monitoring systems to track the model’s performance and detect any drift in data distribution that might require retraining the model. Regularly update the model with new data to maintain its accuracy and relevance. 
   

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