2018 - June 2019, I have been working with Golis Telecom as Network Support Engineer, where I have been repeatedly recognized for developing innovative solutions for great Telecom Power applications and solving a wide range of problems.
Here, I was responsible for the Design, preparation and evaluation of technical power drawings and technical data in the installation of all telecom power equipment. .
I've completed my BSc in Electrical & Electronics Engineering from Kenyatta University, Nairobi, during the period of 2012 - 2017. Over the years i have also completed several comprehensive courses on both machine learning and data science on Udemy and udacity.
Have you noticed that Facebook has developed an uncanny ability to recognize your friends in your photographs? In the old days, Facebook used to make you to tag your friends in photos by clicking on them and typing in their name. Now as soon as you upload a photo, Facebook tags everyone for you like magic. Here is how they do it
Read MoreUse satellite data to track the human footprint in the Amazon rainforest. The goal of this challenge is to label satellite image chips with atmospheric conditions and various classes of land cover/land use. Resulting algorithms will help the global community better understand where, how, and why deforestation happens all over the world - and ultimately how to respond.
Read MoreBike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.
Read MoreA Photovoltaic system is a distributed power generation system that produces electrical power by harnessing solar radiation and converting it into electricity. Solar power generation plants come under the category of Renewable Energy sources as they do not involve the use of fossil fuels such as coal or petroleum for power generation. I have been the Lead electrical engineer responsible for the for th the design and installation of several both small and large scale SOlar PV power projects at Golis Telecom.
Read More
Modern face recognition with deep learning and HOG algorithm.
We are using the Histogram of Oriented Gradients (HOG) method. Instead of computing gradients for every pixel of the image (way too much detail). We compute the weighted vote orientation gradients of 16x16 pixels squares. Afterward, we have a simple representation (HOG image) that captures the basic structure of a face. All we have to do is find the part of our image that looks the most similar to a known trained HOG pattern. For this technique, we use the dlib Python library to generate and view HOG representations of images.
face_detector = dlib.get_frontal_face_detector()
detected_faces = face_detector(image, 1)
Read more on my Github page
Use satellite data to track the human footprint in the Amazon rainforest. The goal of this challenge is to label satellite image chips with atmospheric conditions and various classes of land cover/land use. Resulting algorithms will help the global community better understand where, how, and why deforestation happens all over the world - and ultimately how to respond.
This problem was tackled with Deep Learning models (using TensorFlow and Keras). Submissions are evaluated based on the F-beta score (F2 score), it measures acccuracy using precision and recall.
The real project has in depth and adequate functionalism compared to the above synopsis.
The goal of this challenge is to build a model that predicts the count of bike shared, exclusively based on contextual features. The first part of this challenge was aimed to understand, to analyse and to process those dataset. I wanted to produce meaningful information with plots. The second part was to build a model and use a Machine Learning library in order to predict the count.
The more importants parameters were the time, the month, the temperature and the weather. Multiple models were tested during this challenge (Linear Regression, Gradient Boosting, SVR and Random Forest). Finally, the chosen model was Random Forest. The accuracy was measured with Out-of-Bag Error and the OOB score was 0.85. Please check my Github page for the Code of the project.
