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.

visualization keras cnn dropout classification deeplearning farmers multiclass-classification crops imageclassification field-dataset batchnormalization

• Updated Jun 8, 2019
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𝗖𝘂𝘀𝘁𝗼𝗺-𝗩𝗚𝗚𝟭𝟲 | 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 | 𝗖𝗡𝗡, 𝗕𝗮𝘁𝗰𝗵𝗡𝗼𝗿𝗺, 𝗥𝗲𝗟𝗨, 𝗠𝗮𝘅𝗣𝗼𝗼𝗹, 𝗗𝗿𝗼𝗽𝗼𝘂𝘁, 𝗠𝗡𝗜𝗦𝗧

cnn mnist vgg16 relu batchnormalization maxpool dropout-layers custom-vgg16

• Updated May 3, 2025
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Code to fold batch norm layer of a DNN model in pytorch

deep-neural-networks batchnorm mobilenetv2 batchnormalization

• Updated Mar 8, 2021
• Python

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

transfer-learning batchnorm tensorflow2 batchnormalization

• Updated Apr 2, 2021
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MNIST Digit Prediction using Batch Normalization, Group Normalization, Layer Normalization and L1-L2 Regularizations

cnn pytorch dropout regularization l1-regularization groupnormalization groupnorm batchnormalization layernorm

• Updated Jun 11, 2021
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PyTorch module FLOPS counter

pytorch mobilenetv2 fusing flops batchnormalization flops-counter

• Updated Oct 30, 2024
• Python

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

javascript html flask images cnn machinelearning deeplearning batchnormalization dataaugmentation dropout-keras rendertemplate

• Updated Aug 26, 2023
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This project aims to develop an advanced DL model using CNN to accurately detect and classify brain tumors from MRI scans.

opencv computer-vision deep-learning tensorflow keras cnn-classification sequential-models conv2d earlystopping batchnormalization maxpool2d modelcheckpoint

• Updated Mar 24, 2023
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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.

machine-learning generator deep-learning neural-network tensorflow numpy keras os glob pil discriminator checkpoint generative-adversarial-network gan deep-learning-algorithms imageio matplotlib adam-optimizer matplotlib-pyplot batchnormalization

• Updated Nov 28, 2025
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Step-by-step image classification using Keras—evolving a basic CNN with Batch Norm, Data Augmentation, and Transfer Learning.

python deep-learning cnn vgg16 cnn-keras data-augmentation fine-tuning xception-model cnn-classification xception batchnormalization

• Updated Mar 7, 2026
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Keras Convolutianl Networks

tensorflow ipython dropout matplotlib sequential activation flatten l2 dense conv2d batchnormalization maxpooling2d to-categorical keras-convolutianl-networks glorot-uniform

• Updated Jan 4, 2023
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Charts and Fake image (OCR on Charts)

python opencv machine-learning tensorflow numpy keras pandas image-classification cv2 skimage-ocr pytesseract mathplotlib batchnormalization

• Updated Aug 19, 2022
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tensorflow datascience lstm-neural-network timeseries-forecasting batchnormalization stockmarketforecasting stockmarketanalysis learningrates

• Updated Nov 7, 2024
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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.

tensorflow numpy keras os pandas dropout classification matplotlib image-dataset convolutional-neural-networks flatten dense cnn-classification skin-cancer sequential-models pathlib batchnormalization maxpooling2d sample-images

• Updated Sep 2, 2024
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Deep learning Simple models

deep-learning neural-network keras dropout regularization rmsprop fully-connected-network adam-optimizer sgd-optimizer batchnormalization

• Updated Nov 23, 2022
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Neural networks

artificial-neural-networks convolutional-neural-networks mlp neuralnetwork convolution2d softmax arctan relu numpy-neural-network batchnormalization maxpool2d

• Updated May 26, 2022
• Python

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

deep-learning dropout convolutional-neural-networks cifar-10 batchnormalization dataaugmentation

• Updated Feb 20, 2022
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LeNet-5 to ResNet-50 implemented from scratch in PyTorch — batch normalization ablation study, residual block comparison, trained on FashionMNIST with 90.37% accuracy

python deep-learning cnn pytorch lenet resnet fashion-mnist batchnormalization

• Updated Apr 17, 2026
• HTML

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.

python deep-learning tensorflow keras flatten imagedatagenerator efficientnet batchnormalization streamlit regularizers maxpooling2d

• Updated Apr 22, 2025
• Jupyter Notebook

Fundamentals of Artificial Intelligence and Deep Learning Frameworks

deep-neural-networks deep-learning optimization svm cross-validation pytorch knn backpropagation stochastic-gradient-descent softmax fully-connected-network batchnormalization layernorm

• Updated Nov 2, 2022
• Jupyter Notebook