So, it’s been a while. The past months of my life have been an absolute whirlwind to say the least and updating this site has just not managed to squeeze … Continue reading Bias and Variance and Mean Squared Error
Something Cool about Everything
Did you know that everything can be decomposed into a vector? In theory of course. If we were to consider any abstract object or concept, it would be a combination … Continue reading Something Cool about Everything
Least Squares Regression and the Sum of Squared Residuals
First things first. Technically, I should have written about this before Gradient Descent as Least Squares was the original technique used to find the optimal equation of a linear model … Continue reading Least Squares Regression and the Sum of Squared Residuals
Announcement!
Okay, it’s been a while since I’ve posted. I’ll attest this primarily to my exams at the end of July and further class tests before my IB predicted grades came … Continue reading Announcement!
Deep Learning Libraries – PyTorch Vs Tensorflow 2.0
Before anyone says anything. Yes, I know there are others – Theano for example – but this article is going to focus on the two big players in this space. … Continue reading Deep Learning Libraries – PyTorch Vs Tensorflow 2.0
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Artificial Intelligence by Michael Wooldridge
The title of this article is ripped straight from the book “Artificial Intelligence” by Michael Wooldridge which is what I am going to be discussing in the article today. This … Continue reading Artificial Intelligence by Michael Wooldridge
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Convolutional Neural Networks
First thing I noticed while writing this article is the red underline on convolutional – which really should not be there by now. Convolutional Neural Networks (or CNN’s) are a … Continue reading Convolutional Neural Networks
The IMX500 Artificial Intelligence Chip
A different sort of article today – a little update on the applications of artificial intelligence and more specifically deep learning in the real world. The IMX500 is a joint … Continue reading The IMX500 Artificial Intelligence Chip
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Gradient Descent, Optimizers, and How a Neural Network ‘Learns’
To start off, all three of the above are the same. A Neural Network ‘learns’ by means of its optimizer, and all optimizers are versions of and different implementations of … Continue reading Gradient Descent, Optimizers, and How a Neural Network ‘Learns’
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Artificial Neural Networks
Now here is where things are starting to get amped up. Artificial Neural Networks are the most basic of Neural Network models, implementing layers of artificial neurons (read about them … Continue reading Artificial Neural Networks
The Neural Network 