image source: pinterest

It is fascinating to see the use of one-liner python solutions for complex problems. Maybe it is time for us to take a moment to appreciate the Occam’s Razor Principle:

Non sunt multiplicanda entia sine necessitate (Entities are not to be multiplied without necessity).

image source: giphy

While it is nice to appreciate the simplicity of these solutions, bear in mind the caveat for practical software projects:

Now let’s start with a coding challenge…

Leetcode Problem 17 states:

Given a string containing digits from 2-9 inclusive, return all possible letter combinations that the number could represent. …

all kinds of pasta (Image source)

Every time I visit an Italian restaurant, I struggle in naming all kinds of the food they have. There was three kinds of pasta that I can think of (Lasagna, Gnocchi, Macaroni), but to name 350 kinds of pasta is definitely over the top. In this post we will hack together a pytorch image classifier that is transferable, efficient and accurate.

A quick recap: following the previous post, we have already came to understand:

How to write an ImageClassificationBase(nn.Module) and extend it to a model of your choice;

How and where hyperparameters can be used in the model

How to…


image source

Following the progress on the previous post, this time I want to try to build a deep learning model on more complex form of data: images. To teach anyone to code a deep neural network, I will demonstrate it in by answering this question (see this thread):

what are the three steps to put an elephant into the fridge..?

The task at hand is to predict multiple classes on the type of the object. See below for an example of CIFAR10 dataset. (the classes: 'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship',
'truck' )

Step1: Build a fridge (which can train models)

For any deep learning/machine…


When it comes to machine learning 101, everyone will have to start with regression model. In this model we are trying to answer this question:

Can we use twitter topic statistics to evaluate their topic popularity?

image source (https://images.app.goo.gl/4gFvadPppcokbakR6)

Before we dive into the pool of linear regression, STOP

let’s first review the Safety Measures of Regressions:

What are the assumptions that we have to make before doing regressions?

  • Gauss-Markov theorem helped to explain these basic assumptions on Ordinary Least Squares (OLS), which is the bare bone of Generalized Linear Model (GLM).
  • These assumptions includes: Linearity between X and Y, constant variance of the error, no perfect collinearity, zero conditional mean of the…


Happy quarantine!

I recently started to refresh my knowledge on pytorch. This post is simply to document what I have learned. I will focus on different flavor of matrix multiplication in Pytorch.

Different favors (image source: https://www.mithly.net/tips-for-making-ice-cream-recipes/)
Different favors (image source: https://www.mithly.net/tips-for-making-ice-cream-recipes/)
Different Flavors [image source]

First of all: basics for making ice cream

Cassie Guo

data culinary (data➡️ ingredients; model➡️ recipe; results➡️ delicious dish🥘) and other shenanigans

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