Simple detection on a custom dataset

This is a much shorter article than I’m typically used to writing. I’ve recently been taking the Extensive Vision course with TheSchoolOfAI.

I worked on two small projects using YOLOV3. The first was to run inference on an image of me holding an item present in the coco dataset using pre-trained weights.


Ether supplanting bitcoin matter of when not if

Photo by Thought Catalog on Unsplash

The Flippening refers to the hypothetical moment ether overtakes bitcoin as the most valuable cryptocurrency in terms of market capitalization. Market Capitalisation is the total number of tokens in circulation multiplied by the value of one token

This website tracks and compares the two cryptocurrencies across 9 indicators.

Why exactly does it matter if one cryptocurrency overtakes another?

Before we get into this, let’s do a quick recap of some of the key concepts.

Bitcoin

While there have been many attempts at creating a digital currency, bitcoin is by far the most successful. It is also the inspiration for every cryptocurrency that came after it.

Transactions are verified by network nodes


Semantic Segmentation using ResNet50 Backbone plus Pyramid Pooling

Introduction

I very recently had the privilege and opportunity to participate in a computer vision challenge in partnership with Omdena and WeedBot, an impact-driven startup developing a laser weeding machinery for farmers to find and remove weeds with a laser beam.

Photo by Gunnar Ridderström on Unsplash

We explored Image Segmentation techniques for crops vs weeds classification and explored both Semantic and Instance Segmentation approaches. In this article, we shall be exploring two distinct concepts implemented within the Semantic Segmentation part of the project —

Transfer Learning for Multi-Channel Input

What is Transfer Learning?

Transfer learning is a machine learning technique for the re-use of a pre-trained model on a new problem.

In transfer learning, a…


Network Graph Visualization using TF-IDF, Louvain Clustering, and D3.js

I recently participated in an Omdena AI challenge in partnership with Save the Children, a leading humanitarian organization for children to use natural language processing (NLP) techniques to explore the problem of Online Sexual Violence against children.

Photo by Joao Tzanno on Unsplash

Topic Modeling

Topic modeling is an unsupervised machine learning text analysis technique capable of detecting word and phrase patterns within a collection of documents. The detected patterns are then used to cluster the documents into specific topics. It is a frequently used text-mining tool for discovering semantic structures within text data.

In simple terms, a document about a particular topic will more or less contain…


Boost your Colab Notebook’s GPU

Photo by Sigmund on Unsplash

Before delving into the tutorial. Let’s talk a bit about Jupyter Notebooks.

Jupyter

Jupyter is a free, open-source, shareable, and interactive web application that allows the user to combine code, computational output, visualizations, text, and media.

Jupyter notebooks have become the data scientist’s computational tool of choice. The name is made up of Julia, python, and R, three of the currently supported 40+ programming languages by Jupyter at the moment.

Problems with Notebooks

Notebooks are not all great. Some issues associated with using notebooks include —

Notebooks run into problems when running long asynchronous tasks. Training a model for many hours…


Visualizing Global Legislation with d3.js

Introduction

Some time ago, I participated in an Omdena AI challenge in partnership with Save the Children, a leading humanitarian organization for children with a bold ambition — to create a world where every child has access to quality education and is protected from neglect, exploitation, and abuse.

Photo by Margaret Weir on Unsplash

Over a two-month period, we worked closely with the Technology for Development and Child Protection teams within Save The Children to collect and analyze data from multiple sources including online forums, scientific and newspaper articles for insights.

The Internet — A Dangerous Place

As of January of 2021, there are about 4.6B internet users…


Applying Standard ML algorithms to Time-Series forecasting

Photo By Aron Visuals on Unsplash

We shall be exploring some techniques to transform Time Series data into a structure that can be used with the standard suite of supervised ML models.

Time Series vs Cross-Sectional Data

Time series is a sequence of evenly spaced and ordered data collected at regular intervals. One consequence of this is that there is a potential for correlation between the response variables.

An example of time-series is the daily closing price of a stock. In this example, the observations are of a single phenomenon (stock prices) over a period of time. …

Sijuade Oguntayo

Technical Mentor & Lead ML Engineer @ Omdena

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