All the code files related to the deep learning course from PadhAI

MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.

Implementation of key concepts of neuralnetwork via numpy

Library which can be used to build feed forward NN, Convolutional Nets, Linear Regression, and Logistic Regression Models.

A repository to show how Xavier initialization in Neural Networks help to initialize the weights of the network with random values that are not too small and not too large.

My extensive work on Multiclass Image classification based on Intel image classification dataset from Kaggle and Implemented using Pytorch 🔦

Fully connected network on MNIST data using Tensorflow

Experiments with Tensorflow to Replicate Experiments for the Xavier Initialization Deep Learning Optimization

Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.

My beautiful Neural Network made from scratch and love. It plays the game Flappy-Birds flawlessly, in 3 to 9 generations!!

Xavier Initialization in Deep Learning

Neural Network from Scratch with Python

3-layer linear neural network to classify the MNIST dataset using the TensorFlow

A facial keypoint detection system.