Traditional machine learning assumes that data points are independently and identically dispersed, but in many cases, as with linguistic, speech, and time data, a piece of data depends on those that precede or follow it. Sequence data is another name for this type of information. Also in machine learning, a similar concept of sequencing is followed to learn a sequence of data. In this article, we will understand what Sequential Machine Learning is. We will also see how sequential data is used for modeling purposes and the different models used in sequential machine learning. The main points to cover in this article are listed below.
- What is the sequential model?
- Understanding sequential modeling
- What is sequential data?
- Different sequential models
- RNN and its variants
- Automatic encoders
Let’s start the discussion with the sequential model.
What is sequential learning?
Machine learning models that enter or exit data sequences are called sequence models. Examples of sequential data are text streams, audio clips, video clips, time series data, and other types of sequential data. Recurrent neural networks (RNNs) are a well-known method in sequence models.
Analysis of sequential data such as text sentences, time series, and other discrete sequence data prompted the development of sequence models. These models are better suited for processing sequential data, while convolutional neural networks are better suited for processing spatial data.
The crucial thing to remember about sequence models is that the data we are working with are no longer independently and identically distributed samples (iid), and the data is dependent on each other due to its sequential order. . For speech recognition, speech recognition, time series prediction, and natural language processing, sequence models are particularly popular.
Understanding sequential modeling
Simply described, sequence modeling is the process of producing a sequence of values from a set of input values. These input values can be time series data, which shows how a certain variable, such as demand for a given product, changes over time. Production can be a forecast of demand for future times.
Another example is text prediction, in which the sequence modeling algorithm predicts the next word based on the sequence of the previous sentence and a set of preloaded conditions and rules. Businesses can achieve more than just producing and predicting models using sequence modeling.
What is sequential data?
When the points of the dataset depend on the other points of the dataset, the data is said to be sequential. A time series is a common example, with each point reflecting an observation at a point in time, such as the price of a stock or data from a sensor. Examples of sequential data are sequences, DNA sequences, and meteorological data.
In other words, we can call video data, audio data and images up to a certain point sequential data. Below are some examples of sequential databases.
Below I have listed some popular machine learning apps based on sequential data,
- Time Series: A challenge of predicting time series, such as stock market projections.
- Text mining and sentiment analysis are two examples of natural language processing (for example, learning word vectors for sentiment analysis)
- Machine translation: given a single language entry, sequence templates are used to translate the entry into multiple languages. Here is a recent poll.
- Image captioning evaluates the current action and creates a caption for the image.
- Deep Recurrent Neural Network for Speech Recognition Deep Recurrent Neural Network for Speech Recognition
- Recurrent neural networks are used to create classical music.
- Recurrent neural network to predict transcription factor binding sites based on DNA sequence analysis
In order to model efficiently with this data or to get as much information as possible, it contains a traditional machine algorithm which will not be of much help. To process such data, there are sequential models available and you may have heard of them.
Different sequential model
RNN and its variant-based models
RNN stands for Recurrent Neural Network and is a deep learning and artificial neural network design that is suitable for sequential processing of data. In natural language processing, RNNs are frequently used (NLP). Since RNNs have internal memory, they are particularly useful for machine learning applications that require sequential input. Time series data can also be predicted using RNN.
The main advantage of using RNN instead of conventional neural networks is that the characteristics (weight) of standard neural networks are not shared. In RNN, the weights are shared over time. RNNs can recall their previous entries, unlike standard neural networks. For the calculation, RNN uses historical data.
A different task that can be done using RNN zones,
One by one
With one input and one output, this is the classic feed-forward neural network architecture.
This is called captioning of images. We have a fixed size image as input, and the output can be words or phrases of varying lengths.
This is used to categorize emotions. A succession of words or even paragraphs of words is anticipated as input. The result may be a continuously valued regression output that represents the probability of having a favorable attitude.
Many to many
This paradigm is suitable for machine translation, like the one seen on Google Translate. The input can be a variable length English sentence and the output can be a variable length English sentence in a different language. On a frame-by-frame basis, the latest very many models can be used for video classification.
As you may know, traditional RNNs are not very good at capturing long range dependencies. This is mainly related to the problem of the leakage gradients. Gradients or derivatives decrease exponentially as they descend through layers while forming very deep networks. The problem is called the leakage gradient problem.
To combat the leakage gradient, the LSTM was introduced because its name derives from the problem.
The RNN hidden layer is modified with LSTM. RNNs can remember their entries for a long time using LSTM. In LSTM, a cell state is transferred to the next time step in addition to the masked state.
Long range dependencies can be captured via LSTM. It has the ability to remember previous entries for long periods of time. An LSTM cell has three doors. These gates are used in LSTM to manipulate memory. The propagation of the gradient in the memory of a recurrent network is controlled by gates in long-term memory (LSTM).
For sequence models, LSTM is a common deep learning technique. The LSTM algorithm is used in real-world applications such as Apple’s Siri and Google’s voice search, and is responsible for their success.
One of the most active fields of study in natural language processing is machine translation (MT) (NLP). The objective is to create a computer program capable of quickly and accurately translating a text from one language (source) into another language (target) (the target).
- The encoder section summarizes the data from the source phrase.
- Based on the encoding, the decoder component generates the target language output step by step.
The performance of the encoder-decoder network decreases significantly as the length of the input sentence increases, which is a limitation of these approaches. The fundamental disadvantage of the previous methods is that the encoded vector must capture the full sentence (sentence), which means that a lot of critical information can be missed.
In addition, the data must “flow” through a number of RNN steps, which is difficult for large sentences. Bahdanau et al. introduced an attention layer which consists of attention mechanisms that give some of the input words more weight than others when translating the sentence, which has given new impetus to the apps machine translation.
Seq2seq takes a sequence of words (phrases or sentences) as input and produces a sequence of words as output. It achieves this through the use of a Recurrent Neural Network (RNN). Although basic RNN is rarely used, its more complex variants, such as LSTM or GRU, are. In the planned version of Google, LSTM is used.
By taking two entries at each instant, it builds the context of the word. The recurring name comes from the fact that it receives two inputs, one from the user and one from the previous output (the output goes as input).
It is sometimes referred to as an encoder-decoder network because it mainly consists of two components: an encoder and a decoder.
Encoder: It translates the input words into corresponding hidden vectors using deep neural network layers. Each vector represents the current word as well as its context.
Decoder: It uses the encoder’s hidden vector, its own hidden states, and the current word as input to build the next hidden vector and predict the next word.
Going through these techniques discussed, one could confuse seq2seq and Autoencoder. The input and output domain of the seq2seq model is different (English-Hindi) and used mainly in machine translation applications. While the Autoencoder is a special case of the seq2seq model where the input and output domains are the same (English-English), it behaves like an auto-association, which means that it perfectly recalls or reconstructs the sequence. input if we pass a corrupted sequence. Features like this have taken advantage of the automatic encoder in many applications like model compilation etc.
Through this article, we have seen what a sequential model is. In which we discussed the more fundamental concepts of sequential models and sequential data. In short, we can say that data is sequential if it is anyway associated with time or if its instances are dependent. To process such data, traditional ML algorithms are not very useful because they have to deal with special cases of deep learning technique, as we have seen.
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