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    Neural Networks with R. Build smart systems by implementing popular deep learning models in R

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Neural Networks with R. Build smart systems by implementing popular deep learning models in R Balaji Venkateswaran, Giuseppe Ciaburro - okładka ebooka

    Neural Networks with R. Build smart systems by implementing popular deep learning models in R Balaji Venkateswaran, Giuseppe Ciaburro - okładka ebooka

    Neural Networks with R. Build smart systems by implementing popular deep learning models in R Balaji Venkateswaran, Giuseppe Ciaburro - okładka audiobooka MP3

    Neural Networks with R. Build smart systems by implementing popular deep learning models in R Balaji Venkateswaran, Giuseppe Ciaburro - okładka audiobooks CD

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    Stron:
    270
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    119,00 zł

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    Do przechowalni

    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.

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    O autorach ebooka

    Balaji Venkateswaran is an AI expert, data scientist, machine learning practitioner, and database architect. He has 17+ years of experience in investment banking payment processing, telecom billing, and project management. He has worked for major companies such as ADP, Goldman Sachs, MasterCard, and Wipro. Balaji is a trainer in data science, Hadoop, and Tableau. He holds a postgraduate degree PG in business analytics from Great Lakes Institute of Management, Chennai. Balaji has expertise relating to statistics, classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures. His main interests are neural networks and deep learning. Balaji holds various certifications in IBM SPSS, IBM Watson, IBM big data architect, cloud architect, CEH, Splunk, Salesforce, Agile CSM, and AWS. If you have any questions, don't hesitate to message him on LinkedIn (balvenkateswaran); he will be more than glad to help fellow data scientists.
    Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees holds a master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a master's degreeand in acoustic and noise control from Seconda Università degli Studi di Napoli. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli".He has over 15 20 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer ITC courses (about 15 20 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching machine learning applications in acoustics and noise control. He was recently included in the world's top 2% scientists list by Stanford University.

    Balaji Venkateswaran, Giuseppe Ciaburro - pozostałe książki

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