This Problem is based on a Image Data set consisting of different types of weeds, to detect them in crops and fields. I have used Deep Learning Model called CNN(Convolutional Neural Networks) with Dropout, Batch Normalization, ReduceLearning rate on plateau, Early stoppig rounds, and Transposd Convolutional Neural Networks.

Code to fold batch norm layer of a DNN model in pytorch

Partial transfusion: on the expressive influence of trainable batch norm parameters for transfer learning. TL;DR: Fine-tuning only the batch norm affine parameters leads to similar performance as to fine-tuning all of the model parameters

MNIST Digit Prediction using Batch Normalization, Group Normalization, Layer Normalization and L1-L2 Regularizations

PyTorch module FLOPS counter

Cats vs dogs classification using deep learning. Data augmentation and convolutional neural networks.

This project aims to develop an advanced DL model using CNN to accurately detect and classify brain tumors from MRI scans.

prediction of an absolute temperature on the surface of a star using neural networks

Built a GAN using TensorFlow to generate handwritten digits from the MNIST dataset. Implemented a custom generator, discriminator, training loop, loss functions, and checkpointing, with image generation and GIF animation to visualize model progress across epochs.

Keras Convolutianl Networks

Image classifiaction done on cifar 10 using deep learning (CNN)

Neural networks

batch_normalization through PyTorch

Deep learning Simple models

Exoplanet Hunting in Deep Space.

Fundamentals of Artificial Intelligence and Deep Learning Frameworks

Brain Tumor Detection using EfficientNetB3-based Deep Learning model. The project leverages transfer learning on MRI brain scan images to classify and detect brain tumors with high accuracy. Includes full workflow: data preprocessing, image augmentation, model building, evaluation, and deployment.

Applyed regularization techniques to improvise the performance of VAE Model such as L1/L2 Regularization (Weight Decay), Dropout, Batch Normalization, Beta-VAE (Modified KL Divergence Term), Data Augmentation

Demonstrate how to do backpropagation using an example of BatchNorm-Sigmoid-MSELoss network with a detailed derivation of gradients and custom implementations.