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.

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

This GitHub repository aims to provide comprehensive overview of the evolution of GoogLeNet, a popular Convolutional Neural Network architecture developed by Researchers at Google for image classification tasks.

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

Keras Convolutianl Networks

Skin cancer can be broadly classified into two major categories: Melanoma (Malignant) and non-melanoma (Benign). Melanoma is one of the deadliest kinds of cancer. However, the detection of this cancer at an early stage can help in improving the chances of survival.

Neural networks

batch_normalization through PyTorch

Exoplanet Hunting in Deep Space.

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

Deep learning Simple models

Fundamentals of Artificial Intelligence and Deep Learning Frameworks

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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.