Kostenfreier Download , by Mariya Yao Adelyn Zhou
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, by Mariya Yao Adelyn Zhou
Kostenfreier Download , by Mariya Yao Adelyn Zhou
, By Mariya Yao Adelyn Zhou Wie genau können Sie Ihren Geist sein offenere transformieren? Es gibt zahlreiche Quellen , die Sie Ihre Gedanken steigern helfen könnte. Es kann von einigen Leuten aus den verschiedenen anderen Begegnungen und Geschichte. Spielplan , By Mariya Yao Adelyn Zhou gehört zu dem auf Quellen verlassen zu bekommen. Sie könnten viele Publikationen entdecken , die wir weiter unten auf dieser Seite besprechen. Neben jetzt zeigen wir Ihnen eine der besten, die , By Mariya Yao Adelyn Zhou
Jedes Wort von dem Schriftsteller zu äußern enthält die Komponente dieses Leben. Der Autor wirklich zeigt, wie die grundlegenden Wörter optimal zu nutzen, wie die Wahrnehmung dieses Buches geäußert wird für die Besucher gerade machen könnte. Auch haben Sie eigentlich über das Material von , By Mariya Yao Adelyn Zhou so viel gelernt, können Sie es schnell für viel bessere Verbindung. Bei der Versorgung der Sichtbarkeit von Führungsprinzip, könnten Sie die boo-Website hier entdecken.
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Um die Existenz des Buches zu fördern, unterstützen wir durch die Online-Bibliothek bietet. Es ist in der Tat außer , By Mariya Yao Adelyn Zhou nur; identisch dieses Buch verwandelt sich in eine Sammlung von mehreren Publikationen Kataloge. Die Bücher werden auf Basis von Soft-Dateisystem gegeben, dass die erste Methode für Sie die Beweggründe zu überwinden sein kann, wie auch das Verständnis in weit besseren Szenen neues Leben zu erhalten. Es ist nicht, um Sie wirklich das Gefühl, verwirrt zu machen. Die weichen Dokumente dieser Veröffentlichung kann in bestimmten idealen Werkzeuge gespeichert werden. So kann es reduzieren, wann immer zu lesen.
Produktinformation
Format: Kindle Ausgabe
Dateigröße: 1997 KB
Seitenzahl der Print-Ausgabe: 246 Seiten
Gleichzeitige Verwendung von Geräten: Keine Einschränkung
Verlag: TOPBOTS (2. Juni 2018)
Verkauf durch: Amazon Media EU S.Ã r.l.
Sprache: Englisch
ASIN: B07DHZT2DW
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Durchschnittliche Kundenbewertung:
2.0 von 5 Sternen
1 Kundenrezension
Amazon Bestseller-Rang:
#92.269 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)
Too basic. Reading some reviews and the book description, I was expecting more.If you understand the basics ( I mean very basic general content you can acquire reading some articles) look somewhere else).
I've spent a decent amount of time recently thinking about predictive models, mainly for churn and LTV. As such AI and ML has been something that I've started researching more. I'm not at an advanced level yet, but have spent enough time to know that most of the stuff I'm consuming is simply telling me about the importance of AI and ML as well as the threat or the risk. I haven't found too many practical manuals or frameworks for applying it. I'm not yet through all the frameworks, but in Chapter 8 I've start to be able to outline how to think about what to apply, and what data I'm actually going to need to have. I also like having some of the typical errors around data collection outlined for me. For me, this is super valuable. Will post updates if I have cool tips to share that I've learned.
This is an excellent resource for business owners like myself who are interested in AI, but weren't quite sure how it will change the nature of our work. 'Applied Artificial Intelligence' is keen to note that artificial intelligence is only as good as it's programming. And that programming is inherently biased based on the views of the programmer. To this end, AI programs can produce inconsistent results, even when implemented correctly. I'm not sure if the solution to this is better programming or better understanding of our customers. Since AI talent is scarce, it will take time before these methods will be adapted on a large scale. I agree with the quote about the challenges of AI: "The future is already here, it's just not evenly distributed." - William Gibson. I highly recommend this book to anyone who wants to get a handle on how AI is shaping the business landscape today.
This is a quick read with densely packed information for business leaders looking to understand more about artificial intelligence, its enterprise applications, and recommended implementation strategies. I am using it to aid my transition into an AI-centered company in a design role, so I found the introductory section especially useful in its clarification of domain terminology (e.g. "machine learning" vs. "deep learning", "MLaaS", "probabilistic programming", "AGI" vs "weak AI"). The Machine Intelligence Continuum (MIC) section categorizes machine intelligence by complexity in a way that's useful to non-technical readers. I also appreciated the authors' emphasis on ethical responsibility and the importance of prioritizing inclusivity, for both AI users and creators. The book includes many examples of companies that have already successfully implemented AI strategies, either as core business or in support of existing efforts. This is a well-written summary of what you need to know, with supplementary information available online through the book's website. I highly recommend it for anyone looking to understand more about AI and its potential applications.
I found this book very useful to understanding AI and machine learning from a business perspective. I have some familiarity with the space, and I was still able to get a ton of value from it. The frameworks and examples were incredibly useful, and I especially appreciated that it wasn't overly jargoned or fluffy. Highly recommend!
I worked in government for over a decade making policy decisions about environmental protection and public health. I picked up this book because I’ve been curious about how automation and AI will change our society as well as our governance models. While a lot of the book focuses on corporate applications, I did find the sections on ethical AI, defense against malicious uses of AI, collaborative design of AI systems, and reskilling of displaced workers to be very insightful and relevant for improving public service. I also found it useful to understand the concept of “technical debt†and alternatives to accuracy as measures of how well machine learning systems are performing. Overall, I found this book to be a useful introduction and look forward to learning more about the space.
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