#Type of sequence and examples movie#
#Type of sequence and examples series#

Sequence-to-one (Many-to-one): In sequence-to-one sequence model, the input data is sequence and output data is non sequence.

One classic example is image captioning where input is one single image and the output is a sequence of words. Here is how one-sequence model looks like.

One-to-sequence (One-to-many): In one-to-sequence model, the input data is non sequence and the output data is sequence data.

There are various different types of sequence models based on whether the input and output to the model is sequence data or non-sequence data. For example, given a sequence of words as input, an appropriate Seq2Seq network can generate a new sequence of words that follow a similar pattern or convey a similar meaning. on a large corpus of text data, the network can be used to perform various natural language processing tasks, such as text generation, sentiment analysis, and language translation. The output is hidden state fed back into network and an output state.Īfter training the neural network such as RNN / LSTM / transformer, etc. And, from second input onwards, the hidden state and the next input is fed. The hidden state at each time step captures the context of the current word in the sentence, based on the previous words in the sequence. At each time step, the network processes the current input word (n-dimension vector) and the previous hidden state to generate a new hidden state and an output. Thus, in the above sentence (Climate change…), each word is converted into N-dimension vector where each dimension represents some aspect of the word.Įach of these word embeddings is fed input to the network, one word (n-dimension vector) at a time, in sequence. Each word in the text data is converted as a dense vector of fixed size, where each dimension of the vector corresponds to a particular aspect of the word’s meaning. One way to do this is to use word embeddings, which are numerical representations of words that capture their meaning and context in a language model. To train a neural network such as recurrent neural network (RNN) or long short term memory (LSTM) or transformer network, we need to convert the text data into a numerical representation and feed these embeddings in the network sequentially. In NLP, such sequences of words are often referred to as “ sequences” or “ sequences of tokens“. This is a a sequence of words that convey meaning in a particular order. Let’s take a sentence – Climate change refers to long-term shifts in temperatures and weather patterns. Let’s see an example of sequence data from natural language processing and how are neural networks such as RNN (recurrent neural network) trained with it. How is Sequence Data processed in the Neural Network? The CNN can be used to extract the features from each frame (image) and passed to the sequence models for modeling purpose.

#Type of sequence and examples movie#

Is the movie a sequence data? Yes, the sequence of frames in a movie is an example of sequence data.

Is the text appearing in a sentence, a sequence data? Yes, the text which appears in a sentence is sequence data.However, if the coin is defective, the output can become sequence data. Is the output of flipping a coin a sequence data? Well, if the coin is fair, the output of coin flips is not a sequence data.Lets take a look at some of the example of sequence data points. Here is the example of how sequence data looks like: Sequence data can be represented as observations of one or more characteristics of events over time.

#Type of sequence and examples series#

The time series data is a sequence data which can be defined as a sequence of observations where each observation is dependent on the previous one. The example of sequence data includes time-series data, data related to natural language processing, etc. Sequence data are the data points which are ordered in the meaningful manner such that earlier data points or observations provide the information about later data points or observations and vice versa.

How is Sequence Data processed in the Neural Network?.