# Experience

September 2018 – September 2019
Egham

#### Royal Holloway, University of London

Modules include:

• Machine Learning
• Online Machine Learning
• Deep Learning
• Distributed Systems
• Data Analysis
• Programming in Matlab
February 2018 – October 2018
Tokyo

#### Rakuten

• Analysed the result deeply and define the issue arising in the approach.
• Considered the solution and actively discuss with other team-members.
• Implementated the algorithms to our products.

Resarch Topics:

• Intent Extraction
• Named Entity Extraction
• Deep Learning
• Applied Reinforcement Learning in NLP
June 2015 – February 2018
Tokyo

#### Rakuten

• Extracted data from Database using SQL.
• Analysed the customer behaviour.
• Created the machine learning applied model to enhance the marketing strategies.

# Recent Posts

### Deep Learning implementation using Keras

Introduction Hello everyone! It’s been almost more than a few decades since the theoretical importance of DL was academically proposed. Needless to say, but I have been studying theoretical aspects of DL long. But when it comes to deep understanding, we cannot avoid the actual experiment. So, these days Google or Apple or other IT giants put more efforts for contributing the open source DL libraries enhancing the development. Hence, I would like to study and review keras APIs to show the sample usage of them in this article.

### Google's Interview Instruction for Software Engineer

Foreword I have long been interested in working for Google and been wondering what kind of people could actually pass their interview.. So I have decided to do some research about their content of the interview and also their expectation for candidates. Google’s Interview Process CV Screening: Everyone struggling with this step. Why can’t my CV pass screening for Google and Facebook despite having Amazon in it? Phone/Hangout Screening: Usually takes 30 - 60 minutes with Potential Peer/Manager On-site Interview: Meet Four Googlers for 35-40 minutes each Expectation on Candidates General Cognitive Ability: To explain your answer for open-ended questions in a smart manner, e.

### Gradient Descent Variants ~ Optimisation Algorithms ~

Introduction Gradient descent optimisation algorithms, while increasingly popular, are often used as black-box optimizers, especially when it comes to the actual implementation using some DL libraries. Indeed, practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow us to put them to use. Original Papaer : https://arxiv.org/pdf/1609.04747.pdf

### Independent Q Learning

Profile Titile: MultiAgent Reinforcement Learning: Independent vs Cooperative Agents Author: Ming Tan Published Year: 1993 Link: http://web.media.mit.edu/~cynthiab/Readings/tan-MAS-reinfLearn.pdf Abstraction Since the author got inspired by the learning behaviour of human beings, he investigated the multi-agent in reinforcement learning by comparing two generic assumption. Agents who can cooperate with other agents by sharing information Agent who cannot cooperate with others And he found that basically in the cases described below, agents could efficiently learn through the cooperation each other.