Entries by Yasuto Tamura

Graphical understanding of dynamic programming and the Bellman equation: taking a typical approach at first

This is the second article of the series My elaborate study notes on reinforcement learning. 1, Before getting down on business As the title of this article suggests, this article is going to be mainly about the Bellman equation and dynamic programming (DP), which are to be honest very typical and ordinary topics. One typical […]

Understanding the “simplicity” of reinforcement learning: comprehensive tips to take the trouble out of RL

This is the first article of my article series “My elaborate study notes on reinforcement learning.” *I adjusted mathematical notations in this article as close as possible to “Reinforcement Learning:An Introduction.”  This book by Sutton and Barto is said to be almost mandatory for those who studying reinforcement learning. Also I tried to avoid as […]

Rethinking linear algebra part two: ellipsoids in data science

*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 […]

How to make a toy English-German translator with multi-head attention heat maps: the overall architecture of Transformer

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 […]

Positional encoding, residual connections, padding masks: covering the rest of Transformer components

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 […]

Multi-head attention mechanism: “queries”, “keys”, and “values,” over and over again

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 […]

On the difficulty of language: prerequisites for NLP with deep learning

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 […]

Instructions on Transformer for people outside NLP field, but with examples of NLP

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 […]