*This is the fourth article of my article series “Illustrative introductions on dimension reduction.” 1 Our expedition of eigenvectors still continues This article is still going to be about eigenvectors and PCA, and this article still will not cover LDA (linear discriminant analysis). Hereby I would like you to have more organic links of the […]
If you have been patient enough to read the former articles of this article series Instructions on Transformer for people outside NLP field, but with examples of NLP, you should have already learned a great deal of Transformer model, and I hope you gained a solid foundation of learning theoretical sides on this algorithm. This […]
This is the fourth article of my article series named “Instructions on Transformer for people outside NLP field, but with examples of NLP.” 1 Wrapping points up so far This article series has already covered a great deal of the Transformer mechanism. Whether you have read my former articles or not, I bet you are […]
This is the third article of my article series named “Instructions on Transformer for people outside NLP field, but with examples of NLP.” In the last article, I explained how attention mechanism works in simple seq2seq models with RNNs, and it basically calculates correspondences of the hidden state at every time step, with all the […]
This is the second article of my article series “Instructions on Transformer for people outside NLP field, but with examples of NLP.” 1 Machine translation and seq2seq models I think machine translation is one of the most iconic and commercialized tasks of NLP. With modern machine translation you can translate relatively complicated sentences, if you […]
This is the first article of my article series “Instructions on Transformer for people outside NLP field, but with examples of NLP.” 1 Preface This section is virtually just my essay on language. You can skip this if you want to get down on more technical topic. As I do not study in natural language […]
I found it quite difficult to explain mathematical details of long short-term memory (LSTM) in my previous article series. But when I was studying LSTM, a new promising algorithm was already attracting attentions. The algorithm is named Transformer. Its algorithm was a first announced in a paper named “Attention Is All You Need,” and it […]
In one of my previous articles, I explained the importance of reducing dimensions. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are the simplest types of dimension reduction algorithms. In upcoming articles of mine, you are going to see what these algorithms do. In conclusion, diagonalization, which I mentioned in the last article, is […]
In terms of calculation processes of Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA), which are the dimension reduction techniques I am going to explain in the following articles, diagonalization is what they are all about. Throughout this article, I would like you to have richer insight into diagonalization in order to prepare for […]
This is the first article of the article series Illustrative introductions on dimension reduction. “Curse of dimensionality” means the difficulties of machine learning which arise when the dimension of data is higher. In short if the data have too many features like “weight,” “height,” “width,” “strength,” “temperature”…., that can undermine the performances of machine learning. […]
