Hello, this is day_04 of Deep Learning journey, and this contains what is Perceptron?

How Batch Normalization transforms the internal workings of neural networks by normalizing inputs within each mini-batch. By maintaining stable activations throughout the training process, Batch No…

1. Weight initialization techniques in neural networks 2. Xavier initialization in neural network 3. He initialization in neural network

I'll guide you through weight initialization techniques in neural networks and highlight what NOT to do. From common mistakes to misconceptions, I'll help you navigate the dos and don'ts in optimiz…

Regularization is a set of techniques that can prevent overfitting in neural networks and thus improve the accuracy of a Deep Learning model when facing completely new data from the problem domain.

Explore the power of Dropout Layers with code examples for both Regression and Classification tasks.

Data scaling is a recommended pre-processing step when working with deep learning neural networks. Data scaling can be achieved by normalizing or standardizing real-valued input and output variables.

Vanishing and Exploding Gradient Problems in Artificial Neural Networks (ANNs) with practical code examples. Understand the challenges and solutions for training deep networks effectively. Improve …

Early stopping is a method in Deep Learning that allows you to specify an arbitrarily large number of training epochs and stop training once the model performance stops improving on the validation …

Batch, Stochastic, and Mini-Batch methods

A laptop price predictor, using Ensemble techniques

https://www.kaggle.com/code/manav1703/gre-admission-prediction

Customer Churn Prediction using ANN

Handwritten Digit Classification using ANN

perceptron_hinge_loss_fxn_gradient_descent

perceptron_trick_from_scratch

batch gradient descent from scratch for multiple linear regression

YouTube-link

working with SQL in Pandas

ipl analysis with use of pandas

K-means clustering is a widely used unsupervised learning algorithm that partitions a set of objects into a predetermined number of clusters. The goal is to minimize the sum of the squared distance…