These are the notes I kept while working through my Fundamentals of Electric Circuits course. I start with how to count nodes, branches, and loops, then move through KCL and KVL, voltage and current dividers, and Wye to Delta transformations before getting into Thevenin equivalents and operational amplifier circuits. The last few pages cover the AC side, where I go over phasors, impedance, and why the current lags the voltage by 90 degrees in an inductor. I tried to write each part the way I would explain it to a classmate who missed the lecture.

I put these together during the condensed Machine Learning course I took at Qatar University. They cover the three types of learning, the forward pass of a neural network, and how activation functions and softmax turn raw scores into a probability distribution. From there I work through loss functions, gradient descent, and how backpropagation uses the gradient of the cost with respect to every weight and bias, and I finish with deep computer vision and why convolution and pooling handle images better than a fully connected network.

These are my notes from Physics II, the electricity and magnetism course. I begin with continuous charge distributions and Gauss's law, including a plain argument for why the flux through a closed surface does not depend on its shape, then move into electric potential, current and drift velocity, and the magnetic field of a current carrying wire from the Biot-Savart law. The final chapters deal with inductance and RLC circuits, where I connect the impedance triangle and AC power back to the phasor picture.

Ever wondered how a fighter jet goes from 0 to 250 km/h in just 2 seconds? In this video, I break down the electromagnetic aircraft launch system (EALS), and trace it all the way back to F = IL × B, the fundamental force law governing current-carrying conductors in magnetic fields. I also explored briefly the use of the same concept in Rail Guns, which is a pretty interesting technology.

Actually, its much simpler than you think. It all starts with an inequality that states that the actual area is bigger than the lower limit's area, and is smaller than the higher limit's area. After that, its just a pattern skill to know that you should take the limit of h going to 0.

This tutorial walks you through the process of creating a simple machine learning model on a whiteboard. This model is a simple line with slope m and y-intercept b (bias). In this explanation video, I dive deep into how this connects with real world models, like Computer Vision for example.

Gradients are a fundamental concept in mathematics and machine learning, representing the rate of change of a function with respect to its input variables. In machine learning, gradients are used in optimization algorithms like gradient descent to update model parameters and minimize loss functions.

Where did Imaginary Numbers Come From in AC Circuit Analysis? Watch as I uncover the mathematical origins and practical applications of imaginary numbers in alternating current (AC) circuit analysis.