I am reading a lot of articles about neural networks and I found very different information. I understand that the supervised neural network can be also regression and classification. In both cases I can use the sigmoid function but what is the difference?
A single-layer neural network is essentially the same thing as linear regression. That's because of how neural networks work: Each input gets weighted with a weight factor to produce an output, and the weight factors are iteratively chosen in such a way that the error (the discrepancy between the outputs produced by the model and the correct output that should be produced for a given input) is minimised. Linear regression does the same thing. But in a neural network, you can stack several of such layers on top of each other.
Classification is a potential, but by far not the only, use case for neural networks. Conversely, there are classification algorithms that don't use neural networks (e.g. K-nearest neighbours). The sigmoid function is often used as an activation function for the last layer in a classifier neural network.
I'm doing a language recognizer, I had planned to classify my i-vectors with neural networks, but I've read a lot of papers and they always use other methods like SVM or PLDA, can someone explain to me why? or it's fine to do it with neural networks?
Neural networks are good for complex non-linear multifeature input. I-vectors by design map speaker space to very simple space where speakers are easily separated with logistic regression or SVM.
If you want to try with neural networks, try something end-to-end like https://github.com/FlashTek/vggvox-pytorch
I am interested in machine translation and more specific I would like to examine the similarity between two strings. I would like to know if there are deep learning methods for text feature extraction. I already tried the famous statistics methods like cosine similarity, Levenstein distance, word frequency and others.
Thank you
To find the similarity between 2 string ,try to train a Siamese networks
on your dataset
Siamese networks are a special type of neural network architecture. Instead of a model learning to classify its inputs, the neural networks learns to differentiate between two inputs. It learns the similarity between them.
https://medium.com/#gautam.karmakar/manhattan-lstm-model-for-text-similarity-2351f80d72f1
The below is the link of a kaggle competition ,they have used siamese networks for text simmilarity
https://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07
Hope this clears your doubts
I'm currently trying to create and train a neural network to perform simple speech classification using MFCCs.
At the moment, I'm using 26 coefficients for each sample, and a total of 5 different classes - these are five different words with varying numbers of syllables.
While each sample is 2 seconds long, I am unsure how to handle cases where the user can pronounce words either very slowly or very quickly. E.g., the word 'television' spoken within 1 second yields different coefficients than the word spoken within two seconds.
Any advice on how I can solve this problem would be much appreciated!
I'm currently trying to create and train a neural network to perform simple speech classification using MFCCs.
Simple neural networks do not have input lenght invariance and do not allow to analyze time series.
For classification of time series like a series of MFCC frames you can use a classifier with time invariance. For example you can use neural networks combined with hidden Markov models (ANN-HMM), gaussian mixture model with hidden markov models (GMM-HMM) or recurrent neural networks (RNN). Matlab implementation for RNN is here. Theano implementation is also available. You can find a detailed description of those structures in Google.
Speech recognition is not a simple thing to implement, it is better to use existing software like CMUSphinx
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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 organization of the animal visual cortex, whose individual neurons are arranged in such a way that they respond to overlapping regions tiling the visual field.
CNNs are ideal for images and video processing.
RNN:
RNN can handle arbitrary input/output lengths.
RNN unlike feedforward neural networks - can use their internal memory to process arbitrary sequences of inputs.
Recurrent neural networks use time-series information. i.e. what I spoke last will impact what I will speak next.
RNNs are ideal for text and speech analysis.
Convolutional neural networks (CNN) are designed to recognize images. It has convolutions inside, which see the edges of an object recognized on the image. Recurrent neural networks (RNN) are designed to recognize sequences, for example, a speech signal or a text. The recurrent network has cycles inside that implies the presence of short memory in the net. We have applied CNN as well as RNN choosing an appropriate machine learning algorithm to classify EEG signals for BCI: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
These architectures are completely different, so it is rather hard to say "what is the difference", as the only thing in common is the fact, that they are both neural networks.
Convolutional networks are networks with overlapping "reception fields" performing convolution tasks.
Recurrent networks are networks with recurrent connections (going in the opposite direction of the "normal" signal flow) which form cycles in the network's topology.
Apart from others, in CNN we generally use a 2d squared sliding window along an axis and convolute (with original input 2d image) to identify patterns.
In RNN we use previously calculated memory. If you are interested you can see, LSTM (Long Short-Term Memory) which is a special kind of RNN.
Both CNN and RNN have one point in common, as they detect patterns and sequences, that is you can't shuffle your single input data bits.
Convolutional neural networks (CNNs) for computer vision, and recurrent neural networks (RNNs) for natural language processing.
Although this can be applied in other areas, RNNs have the advantage of networks that can have signals travelling in both directions by introducing loops in the network.
Feedback networks are powerful and can get extremely complicated. Computations derived from the previous input are fed back into the network, which gives them a kind of memory. Feedback networks are dynamic: their state is changing continuously until they reach an equilibrium point.
First, we need to know that recursive NN is different from recurrent NN.
By wiki's definition,
A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structure
In this sense, CNN is a type of Recursive NN.
On the other hand, recurrent NN is a type of recursive NN based on time difference.
Therefore, in my opinion, CNN and recurrent NN are different but both are derived from recursive NN.
This is the difference between CNN and RNN
Convolutional Neural NEtwork:
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. ... They have applications in image and video recognition, recommender systems, image classification, medical image analysis, and natural language processing.
Recurrent Neural Networks:
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.
It is more helpful to describe the convolution and recurrent layers first.
Convolution layer:
Includes input, one or more filters (as well as subsampling).
The input can be one-dimensional or n-dimensional (n>1), for example, it can be a two-dimensional image. One or more filters are also defined in each layer. Inputs are convolving with each filter. The method of convolution is almost similar to the convolution of filters in image processing. In general, the purpose of this section is to extract the features of each filter from the input. The output of each convolution is called a feature map.
For example, a filter is considered for horizontal edges, and the result of its convolution with the input is the extraction of the horizontal edges of the input image. Usually, in practice and especially in the first layers, a large number of filters (for example, 60 filters in one layer) are defined. Also, after convolution, the subsampling operation is usually performed, for example, their maximum or average of each of the two neighborhood values is selected.
The convolution layer allows important features and patterns to be extracted from the input. And delete input data dependencies (linear and nonlinear).
[The following figure shows an example of the use of convolutional layers and pattern extraction for classification.][1]
[1]: https://i.stack.imgur.com/HS4U0.png [Kalhor, A. (2020). Classification and Regression NNs. Lecture.]
Advantages of convolutional layers:
Able to remove correlations and reduce input dimensions
Network generalization is increasing
Network robustness increases against changes because it extracts key features
Very powerful and widely used in supervised learning
...
Recurrent layers:
In these layers, the output of the current layer or the output of the next layers can also be used as the input of the layer. It also can receive time series as input.
The output without using the recurrent layer is as follows (a simple example):
y = f(W * x)
Where x is input, W is weight and f is the activator function.
But in recurrent networks it can be as follows:
y = f(W * x)
y = f(W * y)
y = f(W * y)
... until convergence
This means that in these networks the generated output can be used as an input and thus have memory networks. Some types of recurrent networks are Discrete Hopfield Net and Recurrent Auto-Associative NET, which are simple networks or complex networks such as LSTM.
An example is shown in the image below.
Advantages of Recurrent Layers:
They have memory capability
They can use time series as input.
They can use the generated output for later use.
Very used in machine translation, voice recognition, image description
...
Networks that use convolutional layers are called convolutional networks (CNN). Similarly, networks that use recurrent layers are called recurrent networks. It is also possible to use both layers in a network according to the desired application!