Writing Your Own Python Code to Build a Machine Translator
1. Encoder-decoder Algorithm:
Encoder-decoder algorithm is a widely used approach to acquire machine learning capabilities. The encoder takes the input sequence and converts it into a fixed-length vector representation. This representation is often referred to as a hidden or latent representation. The decoder then takes this hidden representation and generates the output sequence.
The encoder and decoder are typically implemented using a variety of neural networks, such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs), for natural language processing (NLP) tasks, such as machine translation, text summarization, and question answering.
The encoder is usually a recurrent neural network (RNN). RNN encoders are well-suited for sequence-to-sequence (Seq2Seq) tasks because they can learn long-range dependencies between the input and output sequences.
The decoder is typically an RNN or a CNN. RNN decoders are well-suited for generating text because they can learn the sequential relationships between words. CNN decoders are well-suited for tasks that require spatial reasoning, such as image captioning.
The encoder and decoder are typically trained jointly to minimize the error between the predicted output…
