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005    20240129213017.0 
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008    230321s2023    xx 122        o   vleng d 
019    1390763602 
020    9781837638062|q(electronic video) 
020    1837638063|q(electronic video) 
029 1  AU@|b000074214415 
035    (OCoLC)1373633542|z(OCoLC)1390763602 
037    9781837638062|bO'Reilly Media 
040    ORMDA|beng|erda|epn|cORMDA|dOCLCF|dOCLCO 
049    INap 
082 04 025.04 
082 04 025.04|223/eng/20230321 
099    Streaming Video O’Reilly for Public Libraries 
245 00 Recommender systems :|ban applied approach using deep 
       learning.|h[O'Reilly electronic resource] 
250    [First edition]. 
264  1 [Place of publication not identified] :|bPackt Publishing,
       |c2023. 
300    1 online resource (1 video file (2 hr., 2 min.)) :|bsound,
       color. 
306    020200 
336    two-dimensional moving image|btdi|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
344    digital|2rdatr 
347    video file|2rdaft 
380    Instructional films|2lcgft 
500    "Published in February 2023." 
511 0  AI Sciences, presenter. 
520    Recommender systems are used in various areas with 
       commonly recognized examples, including playlist 
       generators for video and music services, product 
       recommenders for online stores and social media platforms,
       and open web content recommenders. Recommender systems 
       have also been developed to explore research articles and 
       experts, collaborators, and financial services. The course
       begins with an introduction to deep learning concepts to 
       develop recommender systems and a course overview. The 
       course advances to topics covered, including deep learning
       for recommender systems, understanding the pros and cons 
       of deep learning, recommendation inference, and deep 
       learning-based recommendation approach. You will then 
       explore neural collaborative filtering and learn how to 
       build a project based on the Amazon Product Recommendation
       System. You will learn to install the required packages, 
       analyze data for products recommendation, prepare data, 
       and model development using a two-tower approach. You will
       learn to implement a TensorFlow recommender and test a 
       recommender model. You will make predictions using the 
       built recommender system. Upon completion, you can relate 
       the concepts and theories for recommender systems in 
       various domains and implement deep learning models for 
       building real-world recommendation systems. What You Will 
       Learn Learn about deep learning and recommender systems 
       Explore the mechanisms of deep learning-based approaches 
       Learn to implement a two-tower model for recommenders 
       Implement TensorFlow to develop a recommender system Learn
       basic neural network models for recommendations Explore 
       neural collaborative filtering and variational 
       autoencoders Audience This course is designed for 
       individuals looking to advance their skills in applied 
       deep learning, understand relationships of data analysis 
       with deep learning, build customized recommender systems 
       for their applications, and implement deep learning 
       algorithms for recommender systems. Individuals passionate
       about recommender systems with the help of TensorFlow 
       Recommenders will benefit from this course. Deep learning 
       practitioners, research scholars, and data scientists will
       also benefit from the course. The prerequisites include a 
       basic to intermediate knowledge of Python and Pandas 
       library. About The Author AI Sciences: AI Sciences is a 
       group of experts, PhDs, and practitioners of AI, ML, 
       computer science, and statistics. Some of the experts work
       in big companies such as Amazon, Google, Facebook, 
       Microsoft, KPMG, BCG, and IBM. They have produced a series
       of courses mainly dedicated to beginners and newcomers on 
       the techniques and methods of machine learning, statistics,
       artificial intelligence, and data science. Initially, 
       their objective was to help only those who wish to 
       understand these techniques more easily and to be able to 
       start without too much theory. Today, they also publish 
       more complete courses for a wider audience. Their courses 
       have had phenomenal success and have helped more than 100,
       000 students master AI and data science. 
588    Online resource; title from title details screen (O'Reilly,
       viewed March 21, 2023). 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Recommender systems (Information filtering) 
650  0 Artificial intelligence. 
650  0 Machine learning. 
650  6 Systèmes de recommandation (Filtrage d'information) 
650  6 Intelligence artificielle. 
650  6 Apprentissage automatique. 
650  7 artificial intelligence.|2aat 
650  7 Artificial intelligence|2fast 
650  7 Machine learning|2fast 
650  7 Recommender systems (Information filtering)|2fast 
655  7 Instructional films|2fast 
655  7 Internet videos|2fast 
655  7 Nonfiction films|2fast 
655  7 Instructional films.|2lcgft 
655  7 Nonfiction films.|2lcgft 
655  7 Internet videos.|2lcgft 
655  7 Films de formation.|2rvmgf 
655  7 Films autres que de fiction.|2rvmgf 
655  7 Vidéos sur Internet.|2rvmgf 
710 2  AI Sciences (Firm),|epresenter. 
710 2  Packt Publishing,|epublisher. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/videos/~/9781837638062/?ar|zAvaialbe 
       on O'Reilly for Public Libraries 
994    92|bJFN