Category: Artificial Intelligence

Unsupervised Learning
As I said – Imagine learning without a teacher, with no labelled inputs or help from the user. We have taken an unlabeled raw input data, which means it is not categorized and corresponding outputs are also not given. Now, this unlabeled input data is fed to the machine learning model in order to train […]

Types of Learnings in ML
Broadly there are three different types of Machine learning algorithms. (1) Unsupervised Learning : There is no help from the user for the computer to learn. Imagine learning without a teacher or any sort of guidance. (2) Supervised Learning : Imagine learning with a teacher. The process of an algorithm learning from the training dataset […]

Backpropagation Algorithm
Now this is the concept my entire computational neuroscience study was essentially based on. Backpropagation is a short form for “backward propagation of errors.” Basically, back propagation means that after each forward pass, the network performs a backward pass, where it adjusts the weights and biases – in order to minimise the cost function. The cost […]

Gradient Descent
Gradient Descent is an optimization algorithm used in Machine Learning, one of the most popular ones! What is a Gradient? “A gradient measures how much the output of a function changes if you change the inputs a little bit.” — Lex Fridman (MIT) A gradient basically calculates the change in weights in regard to change in […]

Parameter Initialisation in AI
We always weights and biases in a neural network. But what are they? Weights: It decides how much influence the input will have on the output.Biases : An anomaly in the output of machine learning algorithms. They are usually zero. Now if you are working with a neural network model (once you have chosen number […]

Neural Networks!
Can be complicated sometimes, eh? Let’s make it super simple for you! Structure of neural networks – Input neurons represents the information we are trying to classify. – Each number in the input neurons is given a weight at each synapse– At each neuron in the next layer, we add the outputs of all synapses coming […]