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).
While it is nice to appreciate the simplicity of these solutions, bear in mind the caveat for practical software projects:
Leetcode Problem 17 states:
Given a string containing digits from 2-9 inclusive, return all possible letter combinations that the number could represent. …
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
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',
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:
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?
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.
data culinary (data➡️ ingredients; model➡️ recipe; results➡️ delicious dish🥘) and other shenanigans