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Probability and Statistics for Deep Learning

By the end of this note, you will understand random variables, expectation, variance, and key distributions, apply Bayes’ theorem and MLE in ML/DL settings, explain the bias–variance tradeoff and its link to overfitting and underfitting, and use core information theory concepts like entropy and KL divergence in loss functions and model evaluation.

Deep Learning - A Comprehensive Guide

JSR

Feb 23, 2026

25 minutes read

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Backpropagation

Backpropagation (or backprop) is the algorithm used to compute the gradient of the loss with respect to every parameter in a neural network. It does this by applying the chain rule layer by layer, starting from the loss at the output and moving backward through the network.

Mar 15, 2026

40 min read

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Optimizers

An optimizer (or optimization algorithm) is the component that takes the gradients of the loss with respect to the parameters (computed by backpropagation) and produces the actual parameter updates.

Mar 15, 2026

~40 min read

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Loss Functions

A loss function (or cost function) is a scalar function that measures how wrong the model’s predictions are compared to the true targets. It takes predictions and targets as inputs and outputs a single number: the higher the loss, the worse the model is doing.

Mar 15, 2026

40 min read

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Activation Functions

Think of a volume knob that doesn’t just multiply the signal linearly: it might squash loud sounds (saturation), cut off negative values (ReLU), or smoothly compress everything into a fixed range (sigmoid).

Mar 15, 2026

40 min read

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Artificial Neuron And Perceptron

An artificial neuron is the smallest computational unit in a neural network. It takes several numeric inputs, multiplies each by a weight, adds a bias, and passes the result through an activation function to produce one output.

Mar 5, 2026

45 min read

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Optimization Fundamentals

Optimization in machine learning and deep learning is the process of finding parameter values (e.g., weights) that minimize a loss function

Mar 5, 2026

35 minutes read

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Calculus For Deep Learning

By the end of this note, you will understand derivatives, gradients, and the chain rule, how they power backpropagation and gradient descent, when to use derivative vs gradient vs Jacobian, and how to answer key deep learning calculus interview questions clearly and correctly.

Mar 4, 2026

35 minutes read

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