This repo contains my work & The code base for this Deep Learning Specialization offered by deeplearning.AI

Younger

Equation Learning Based Neural Network Model

Front End for the Datasets of Yangs Cloud

MobileViViT, a higher dimensional adaptation of MobileViT

This research presents a hybrid deep learning framework combining MobileNet V2 with LSTM, GRU, and Bidirectional LSTM for classifying various potato diseases. The study explores the performance of different architectures to determine the optimal configuration for accurate disease categorization.

Custom ResNet18 1D Network with Improved Architecture

This project aims at creating a neural network from scratch, to apprehend not only the theoretical part but also the practical part

Bottleneck ResNet16 1D Network with Enhanced Architecture

This project demonstrates how to build an image classification model using Convolutional Neural Networks (CNNs) to classify images into predefined categories. It covers data preprocessing, model building, training, and evaluation steps.

simple PyTorch neural network for binary classification on synthetic data with training and evaluation.

Custom ResNet18 2D Network with Improved Architecture

Deep Learning Specialization on Coursera (offered by deeplearning.ai)

Bottleneck ResNet16 2D Network with Enhanced Architecture

Bottleneck ResNet16 3D Network with Enhanced Architecture

This repository implements a 3-layer neural network with L2 and Dropout regularization using Python and NumPy. It focuses on reducing overfitting and improving generalization. The project includes forward/backward propagation, cost functions, and decision boundary visualization. Inspired by the Deep Learning Specialization from deeplearning.ai.

Repository for the Deep Learning Specialization by DeepLearning.AI on Coursera.

Custom ResNet18 3D Network with Improved Architecture

Bottleneck ResNet16 3D Network using Conv2Plus1D layers with Enhanced Architecture

Custom ResNet18 3D Network using Conv2Plus1D layers with Improved Architecture