recurrent-neural-network

Can someone please explain this? I know bidirectional LSTMs have a forward and backward pass but what is the advantage of this over a unidirectional LSTM? What is each of them better suited for? LSTM in its core, preserves information from inputs that has already passed through it using the hidden state. Unidirectional LSTM only preserves information of the past because the only inputs it has seen are from the past. Using bidirectional will run your inputs in two ways, one from past to future and one from future to past and what differs this approach from unidirectional is that in the LSTM

In this blog post, The Unreasonable Effectiveness of Recurrent Neural Networks , Andrej Karpathy mentions future directions for neural networks based machine learning: The concept of attention is the most interesting recent architectural innovation in neural networks. [...] soft attention scheme for memory addressing is convenient because it keeps the model fully-differentiable, but unfortunately one sacrifices efficiency because everything that can be attended to is attended to (but softly). Think of this as declaring a pointer in C that doesn't point to a specific address but instead defines

I'm new to the topic of neural networks. I came across the two terms convolutional neural network and recurrent neural network . I'm wondering if these two terms are referring to the same thing, or, if not, what would be the difference between them? Difference between CNN and RNN are as follows: CNN: CNN takes a fixed size inputs and generates fixed-size outputs. CNN is a type of feed-forward artificial neural network - are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing. CNNs use connectivity pattern between its neurons and is inspired by the

I am brand new to Deep-Learning so I'm reading though Deep Learning with Keras by Antonio Gulli and learning a lot. I want to start using some of the concepts. I want to try and implement a neural network with a 1-dimensional convolutional layer that feeds into a bidirectional recurrent layer (like the paper below). All the tutorials or code snippets I've encountered do not implement anything remotely similar to this (e.g. image recognition) or use an older version of keras with different functions and usage. What I'm trying to do is a variation of this paper : (1) convert DNA sequences to one

I'm trying out Tensorflow's rnn example . With some problems at the start I could run the example in order to train the ptb and now I have a model trained. How do I exactly use the model now to create sentences without having to train every time again? I'm running it with a command like python ptb_word_lm.py --data_path=/home/data/ --model medium --save_path=/home/medium Is there a example somewhere on how to use the trained model to make sentences? 1.Add the following code at the last line of PTBModel:__init__() function: self._output_probs = tf.nn.softmax(logits) 2.Add the following function