The difference between music and speech is crystal clear to human ears, but how do you train a machine to learn the same?
My goal is to create a classifier that can differentiate between music and speech.
I based my approach and model off of this TensorFlow tutorial, which builds a speech recognition network that recognizes 10 different keywords:
For this project, I’ll use the GTZAN music speech dataset:
With the amount of waste produced daily worldwide, waste management is a massive problem. A significant part of this issue is waste classification. But what if we could use AI to automate the process?
My goal is to build machine learning models that can classify waste as organic or recyclable from images.
Similar to my past article on Pokémon classification, I’ll do this using a convolutional neural network.
This is the dataset that I’ll be using:
It is split into test and train directories that are both further…
With the impacts of climate change ever-increasing, understanding the current state of clean technology is vital. By taking a look at where we are at and where we are going, we can figure out what we need to be doing today to prevent a climate disaster.
My goal is to explore and visualize clean technology data in various countries using Python.
To do this, I’ll use pandas, a Python library for data manipulation and analysis.
For this project, I’ll use three data sources:
Sarcasm can be incredibly difficult to spot over the internet. So, why not train machine learning models to discern it for us?
In this article, I go over how to build machine learning models that can detect sarcasm in news headlines.
I based my data preprocessing and deep learning model on the steps shown in this text classification tutorial by Google Developers:
I used version 1 of this dataset for this project:
As a machine learning beginner and Pokémon fan, what better way to explore data visualization tools and neural networks than Pokémon classification?
My goal is to build a classifier that can predict whether a Pokémon is a fire-type or a water-type based on its image.
To do this, I’ll use a convolutional neural network, a class of deep neural networks most commonly applied to analyzing visual imagery.
I’ll be using this dataset from Kaggle:
It contains images of all Pokémon from generation 1 to 7…