I want to ask the LSTM can be modeled as many-to-one.
However, Seq2Seq can also be modeled as many-to-one. (M to N, when N is one).
So, what is the difference?
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can not only process single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems).
(https://en.wikipedia.org/wiki/Long_short-term_memory)
Seq2seq is a family of machine learning approaches used for language processing. Applications include language translation, image captioning, conversational models and text summarization.
...
Seq2seq turns one sequence into another sequence. It does so by use of
a recurrent neural network (RNN) or more often LSTM or GRU to avoid
the problem of vanishing gradient.
(https://en.wikipedia.org/wiki/Seq2seq)
From my understanding, I guess Seq2Seq is a model which is optimized for NLP and uses an LSTM or GRU under the hood.
Related
I would like to implement an artificial neural network via Hebbian learning for MNIST multiclass classification.
Is there a way to set or change learning rules in Keras for define an Hebbian ANN?
Or a method to change the update of the weights of the network based on a rule decided by me instead of the classic Keras settings?
Otherwise I would have thought of defining a neural network without the aid of the libraries but there would certainly be less accuracy and I would not have the practicality given by the libraries
i am new to programming, python and all. i am tasked with a work at school that requires me to develop and evaluate abusive language detection models from a given dataset. my proposed model must be a Convolutional Neural Network with an appropriate embedding layer as a first layer. my problem is i don't know how to start as i am very new to this with no prior knowledge
First, you can start reading and have understanding of a CNN first.
https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
Lastly, you can check few sample implementation here
Keras:
https://towardsdatascience.com/building-a-convolutional-neural-network-cnn-in-keras-329fbbadc5f5
Pytorch:
https://adventuresinmachinelearning.com/convolutional-neural-networks-tutorial-in-pytorch/
Tensorflow
https://www.tensorflow.org/tutorials/images/cnn
What would be the best way to train a neural network with CNTK and with progressive growing? I'm referring to the method described in Progressive Growing of GANs for Improved Quality, Stability, and Variation.
The network is first trained with smaller resolutions. After a while new convolutional layers are added to the network that operate at higher resolutions. The already trained parameters of the lower resolution part need to be there after adding the new layers.
Is there an easy way to add the layers and transfer the already learned parameters?
Lately I was on a Data Science meetup in my city, there was a talk about connecting Neural Networks with SVM. Unfortunately presenter had to quit right after presentation, so I wasn't able to ask some questions.
I was wondering how is that possible ? He was talking about using neural networks for his classification, and later on, he was using SVM classifier to improve his accuracy and precision by about 10%.
I am using Keras for Neural Networks and SKlearn for the rest of ML.
This is completely possible and actually quite common. You just select the output of a layer of the neural network and use that as a feature vector to train a SVM. Generally one normalizes the feature vectors as well.
Features learned by (Convolutional) Neural Networks are powerful enough that they generalize to different kinds of objects and even completely different images. For examples see the paper CNN Features off-the-shelf: an Astounding Baseline for Recognition.
About implementation, you just have to train a neural network, then select one of the layers (usually the ones right before the fully connected layers or the first fully connected one), run the neural network on your dataset, store all the feature vectors, then train an SVM with a different library (e.g sklearn).
I need to implement a classification application for neuron-signals. In the first step, I need to train a denoising autoencoder(DAE) layer for signal cleaning then, I will feed the output to a DBN network for classification. I tried to find support for these types in Tensorflow but all what I found was two models CNN and RNN. Does anyone has an idea about a robust implementation for these two models using Tensorflow?