Using existing models, not just aid machine learning engineers or data scientists but also helps companies to save computational costs as it requires less training. I believe this is why we need to learn about data science, about deep learning – by knowing we are facing, and we are figuring.When you are working on a Machine learning problem, adapting an existing solution and repurposing it can help you get to a solution much faster. As we are enjoying the fruit brought by technology, society is facing more challenges. The wheel of history is rolling on, and there’s no way for it to go back. As a result, it poses a problem to the misuse of language auto-generation, that they might be misused to write fake news, sway people’s opinions, and so on. However, soon, people can hardly differentiate between auto-generated sentences and human-generated ones. Right now, the models are not perfect, that we can realize the sentences are auto-generated by reading them. For example, with the information of a company listed, the model can generate a marketing piece. Transformers can also be applied to language translation and automatic language generator. BERT, as a popular transformer, is used in journalism to generate news. The model can be useful in entertainment, education, and training. As a result, an automatic chatbot is born. How can the model be applied to business? If I have ten years of customer support chatbot conversation, we can train the model on that. For example, if the output wants to generate “breakfast,” it would see how the word is related to “eggs, bagels, and cheese” in the input. The job of this layer is to tie the output word with the input word. For example, while generating the sentence, “Tom felt bloated after the heavy breakfast.” If the transformer wants to generate the word “heavy,” the self-attention layer will want to check how much it is related to “bloated.” The second attention layer is the Enc-Dec attention layer. It checks how related the word it wants to generate is related to the words around. Self-attention layer measures intra-sentence agreement. The encoder and decoder both have attention layers. Attention, which is a term commonly used here, refers to the operation that some words are placed more emphasis. The layers, which represent various mathematical operations, convert the matrices and send them to the decoder. Each word is represented as a 300-D vector. What happens inside? The encoder and decoder have various layers, and at each layer, some transformations happen to a vector or matrix.

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