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005    20240129213017.0 
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008    230320s2023    xx 247        o   vleng d 
020    9781803242828|q(electronic video) 
020    1803242825|q(electronic video) 
029 1  AU@|b000074212488 
035    (OCoLC)1373596569 
037    9781803242828|bO'Reilly Media 
040    ORMDA|beng|erda|epn|cORMDA|dOCLCF|dOCLCO|dALSTP 
049    INap 
082 04 006.3/1 
082 04 006.3/1|223/eng/20230320 
099    Streaming Video O’Reilly for Public Libraries 
245 00 Deep learning.|pRecurrent neural networks with TensorFlow.
       |h[O'Reilly electronic resource] 
246 30 Recurrent neural networks with TensorFlow. 
264  1 [Place of publication not identified] :|bPackt Publishing,
       |c[2023] 
300    1 online resource (1 video file (4 hr., 7 min.)) :|bsound,
       color. 
306    040700 
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 
511 0  Lazy Programmer, presenter. 
520    Recurrent Neural Networks are a type of deep learning 
       architecture designed to process sequential data, such as 
       time series, text, speech, and video. RNNs have a memory 
       mechanism, which allows them to preserve information from 
       past inputs and use it to inform their predictions. 
       TensorFlow 2 is a popular open-source software library for
       machine learning and deep learning. It provides a high-
       level API for building and training machine learning 
       models, including RNNs. In this compact course, you will 
       learn how to use TensorFlow 2 to build RNNs. We will study
       the Simple RNN (Elman unit), the GRU, and the LSTM, 
       followed by investigating the capabilities of the 
       different RNN units in terms of their ability to detect 
       nonlinear relationships and long-term dependencies. We 
       will apply RNNs to both time series forecasting and NLP. 
       Next, we will apply LSTMs to stock "price" predictions, 
       but in a different way compared to most other resources. 
       It will mostly be an investigation about what not to do 
       and how not to make the same mistakes that most blogs and 
       courses make when predicting stocks. By the end of this 
       course, you will be able to build your own build RNNs with
       TensorFlow 2. What You Will Learn Learn about simple RNNs 
       (Elman unit) Covers GRU (gated recurrent unit) Learn how 
       to use LSTM (long short-term memory unit) Learn how to 
       preform time series forecasting Learn how to predict stock
       price and stock return with LSTM Learn how to apply RNNs 
       to NLP Audience This course is designed for anyone 
       interested in deep learning and machine learning or for 
       anyone who wants to implement recurrent neural networks in
       TensorFlow 2. One must have decent Python programming 
       skills, should know how to build a feedforward ANN in 
       TensorFlow 2, and must have experience with data science 
       libraries such as NumPy and Matplotlib. About The Author 
       Lazy Programmer: The Lazy Programmer is an AI and machine 
       learning engineer with a focus on deep learning, who also 
       has experience in data science, big data engineering, and 
       full-stack software engineering. With a background in 
       computer engineering and specialization in machine 
       learning, he holds two master's degrees in computer 
       engineering and statistics with applications to financial 
       engineering. His expertise in online advertising and 
       digital media includes work as both a data scientist and 
       big data engineer. He has created deep learning models for
       prediction and has experience in recommendation systems 
       using reinforcement learning and collaborative filtering. 
       He is a skilled instructor who has taught at universities 
       including Columbia, NYU, Hunter College, and The New 
       School. He has web programming expertise, with experience 
       in technologies such as Python, Ruby/Rails, PHP, and 
       Angular, and has provided his services to multiple 
       businesses. 
588    Online resource; title from title details screen (O'Reilly,
       viewed March 20, 2023). 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
630 00 TensorFlow. 
650  0 Machine learning. 
650  0 Neural networks (Computer science) 
650  0 Artificial intelligence. 
650  6 Apprentissage automatique. 
650  6 Réseaux neuronaux (Informatique) 
650  6 Intelligence artificielle. 
650  7 artificial intelligence.|2aat 
650  7 Artificial intelligence|2fast 
650  7 Machine learning|2fast 
650  7 Neural networks (Computer science)|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  Lazy Programmer,|epresenter. 
710 2  Packt Publishing,|epublisher. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/videos/~/9781803242828/?ar|zAvaialbe 
       on O'Reilly for Public Libraries 
938    Alexander Street|bALSP|nASP5498818/marc 
994    92|bJFN