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2-10-4 Recurrent Neural Networks
Recurrent Neural Networks come into picture when there's a need for predictions using sequential data.
Recurrent neural networks address this issue.
Recurrent Neural Networks have loops
These loops make recurrent neural networks seem kind of mysterious.،Keywords: Video Captioning , NLP , CNN ,RNN ,TDL Module ,
(2.4) General Overview of an Encoder-Decoder Based Framework Fig (2.5) Classification of Different Deep Learning Methods in Video Captioning Fig(2.6) General overview of CNN - RNN Video Caption Generation Method.
(2. 7) A General Overview of Video Caption Generation Method where RNN is used in Encoding Stage.
Furthermore, RNNs were employed to encode the video by feeding the picture properties of the frames into LSTM [19].
The extraction of visual features using a pre-trained Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN)-based model and then feeding that to an RNN-based language model to generate variable-length output vector is a general approach to single sentence video captioning for time-varying inputs.
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Recurrent Neural Networks. 379
We will discuss more advanced top‐ ics as we go through the following chapters, in particular many operations related to deep neural networks, convolutional neural networks, and recurrent neural networks as well as how to scale up with TensorFlow using multithreading, queues, multiple GPUs, and multiple servers.
Then we will explore a few other popular neural network architectures: convolutional neural networks, recurrent neural networks, and autoen‐ coders.13
This is the exploding gradients prob‐ lem, which is mostly encountered in recurrent neural networks (see Chapter 14).
A popular technique to lessen the exploding gradients problem is to simply clip the gradients during backpropagation so that they never exceed some threshold (this is mostly useful for recurrent neural networks; see Chapter 14).
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