Neural Networks And Nebbiolo
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Neural Networks and Nebbiolo
This book is a proof of concept for how artificial intelligence could, and should be applied to each and every aspect of the wine industry from vine to wine, to assist wine professionals in improving their professional skills, productivity, and efficiency, to change the wine industry for the better, and ultimately enrich wine consumers' experiences. We ask, answer, illustrate, and demonstrate the solutions to a diverse range of questions relevant to wine professionals and enthusiasts, including but not limited to: How could AI be leveraged for improving viticulture such as vineyard management and natural disaster response? What are the essential components and techniques to enable speech assistants like Alexa or Google Home to answer any wine-related questions? How could AI automatically come up with reasonable wine pairing suggestions, whether it be with food, music, or art? How could AI help flying winemakers and globe-trotting wine professionals optimize their lifelong wine experiences? How could AI techniques tailor and optimize for each wine taster the best blind tasting strategies based on personal strengths and weaknesses? What factors could influence bidder behaviors at wine auctions, and which auction design elements play a role in auctioneers' expected revenue from the auction? How could AI methods help design the optimal auction mechanism for the auctioneer? Are fine and rare wines worth considering of potential alternative assets relative to traditional assets for investment? How could AI improve wine collector's investment portfolio management strategies? Could AI assist vine-growers, viticulturists, and geneticists in accurately identifying grape varieties around the globe? What makes a great wine list? How to leverage AI to automatically evaluate and generate wine lists according to themes, preferences, moods, and occasions? What are some AI techniques that would enable us to automatically generate wine maps according to any artistic styles? How could we scientifically pinpoint the causal effect of Terrior versus Vigneron on wine? What are some AI techniques that would enable us to know for sure if wine's quality is caused by winemaking practices, vintage variations, climats or lieux-dits, etc.? How could we automatically generate creative cocktail recipes with AI?
Neural Networks with R
Author: Giuseppe Ciaburro
language: en
Publisher: Packt Publishing Ltd
Release Date: 2017-09-27
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
Artificial Intelligence Books For Beginners
Author: Dr Bhawana Pillai, Prof. Priyank Nayak, Prof Vijendra Palash, Prof Priyanka Parihar
language: en
Publisher: Blue Rose Publishers
Release Date:
Artificial intelligence is a field of computer science that focuses on the development of intelligent machines capable of performing tasks that would typically require human intelligence. Remember that AI is a vast and evolving field, and this is just a brief introduction to some key concepts. There are numerous resources available, including online and This books, that can provide more in-depth knowledge for beginners interested in artificial intelligence.