Understanding the Dataset & DataLoader in PyTorch

Update on 9-Apr-2020

  1. I had an opportunity to present regarding Faster R-CNN. The slides can be found here. Note, I adapted figures from multiple sources (inc. textbooks, blog posts, etc); the original material can be found from links on the slides.


After some surveys, I thought

“Yes! This tutorial explains it very well, and the…

What is EMI?

EMI-10: Python. Generator

# list_of_int.txt

Example 10.1: Create a simple function

def int_gen_func(filename):
"""A simple…

Photo by Stephen Dawson on Unsplash

In a business context, we are often interested in creating dashboards, which enable us to show images, graphs, tables, etc. There are many frameworks to create dashboards (a.k.a. frontend applications). For Python users, Plotly/Dash would be one of the options. Regarding platforms, Google Cloud Platform (GCP) provides a fully managed serverless platform, App Engine, where we can readily deploy a frontend application.

In a previous article, we discussed how to deploy a Flask app on Cloud Run with authentication. With the previous example, we are going to demonstrate two things:

  1. how to deploy a Dash application on AppEngine;
  2. how to…

Miscellaneous notes related to coding, machine learning, data science

Already a month has passed from the last post of EMI. Here are some findings from my daily work.

EMI-5: Python. Jupyter notebook, execute Terminal command

echo command on Jupyter Notebook
├── img_1.png
├── img_2.png
├── img_3.png
└── img_4.jpg

EMI-6: Python. Find the longest sub-list from the main-list.

# main list
m_l = [[32, 37, 4, 999999999, 43]…

Authorised users only

Recently I had the opportunity to learn how to host a Flask application on Google Cloud Platform (GCP) using Cloud Run and Cloud Endpoints. Though official documentation is provided, it took me some time to understand and implement the various components correctly. In this article, I am going to show you how to deploy a Flask app on Cloud Run with authentication. Let’s deploy a web application on GCP with an authentication process.

Miscellaneous notes related to coding, machine learning, data science


Recently, I learnt many things from my work, however, I realised that I have rarely re-visited what I learnt; therefore, I sometimes forget how to solve a problem which I came across before. This series of post (hope it continues) will be memorandums for my work, in the area of data science, coding, and machine learning.

Rensei: drilling, training

EMI-1: Python. Comprehension and generator


Photo by Markus Winkler on Unsplash

As a former electrical engineering student, my ‘go-to’ language has always been Matlab. Matlab is great for numerical analysis (including implementing deep learning models with recent updates); however, Matlab is not free.

During my undergraduate studies, I learnt Python. Python is one of the most popular programming languages, especially in the field of data science; it has many built-in functions and modules to facilitate data analysis.

In fact, my ‘go-to’ language has recently been shifting to Python. I am falling in love with Python. …

Takashi Nakamura, PhD

Data scientist and machine learning engineer. PhD in Signal Processing for Neuroscience. https://www.linkedin.com/in/takashi-nakamura-004875a6/

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