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2d-convolution

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TF-1D-2D-ResNetV1-2-SEResNet-ResNeXt-SEResNeXt

Sakib1263 / TF-1D-2D-ResNetV1-2-SEResNet-ResNeXt-SEResNeXt

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Models supported: ResNet, ResNetV2, SE-ResNet, ResNeXt, SE-ResNeXt [layers: 18, 34, 50, 101, 152] (1D and 2D versions with DEMO for Classification and Regression).

demo machine-learning deep-learning pipeline jupyter-notebook python3 resnet regression-models resnext 2d-convolution keras-tensorflow classification-model 1d-convolution seresnet seresnext tensorflow2
  • Updated Jan 27, 2022
  • Jupyter Notebook

Sakib1263 / Inception-InceptionResNet-SEInception-SEInceptionResNet-1D-2D-Tensorflow-Keras

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Models Supported: Inception [v1, v2, v3, v4], SE-Inception, Inception_ResNet [v1, v2], SE-Inception_ResNet (1D and 2D version with DEMO for Classification and Regression)

machine-learning deep-neural-networks deep-learning regression python3 classification convolutional-neural-networks 2d-convolution keras-tensorflow inception-v3 inceptionv2 1d-convolution inception-resnet-v2 inception-v4 tensorflow2 auxiliary-learning googlenet-inception-network inception-resnet-v1 seinception seinceptionresnet
  • Updated Feb 9, 2022
  • Jupyter Notebook
Noise2Noise-Lite-two-ligther-versions-of-the-famous-AI-denoiser-for-small-images

EliaFantini / Noise2Noise-Lite-two-ligther-versions-of-the-famous-AI-denoiser-for-small-images

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Noise2Noise is an AI denoiser trained with noisy images only. We implemented a ligther version which trains faster on smaller pictures without losing performance and an even simpler one where every low-level component was implemented from scratch, including a reimplementation of autograd.

python pytorch autograd unet noise-reduction 2d-convolution adam-optimizer sigmoid-function mean-square-error noise2noise pytorch-framework pytorch-implementation denoiser denoiser-network relu-activation autograd-engine sequential-pytorch
  • Updated Sep 25, 2022
  • Jupyter Notebook

csbanon / mnist-classifiers

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A collection of Jupyter notebooks containing various MNIST digit and fashion item classification implementations using fully-connected and convolutional neural networks (CNNs) built with TensorFlow and Keras. 2020.

computer-vision deep-learning neural-network cnn mnist classification convolution convolutional-neural-network 2d-convolution max-pooling multi-class-classification fully-connected 2d-max-pooling
  • Updated Nov 12, 2020
  • Jupyter Notebook

Robert-E-630 / Classifying_Sentence_Toxicity

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NLP Project - Sentence Classification - Toxicity- Approx 20,000 comments - ranging from 2 to 30 words. Balanced Data Set. 1. Traditional, pre-2010 NLP and ML techniques used. 2. Dense Word Vectors - w2v & Glove, sentence vector created from averaged word vectors, ANN. 3. Glove combined with bi-LSTMs and 2D Convs.

tf-idf sentence-classification word2vec-model nlp-machine-learning 2d-convolution lstm-neural-networks sequence-classification bi-directional bi-lstm glove-embeddings sentence-embeddings
  • Updated Dec 25, 2021
  • Jupyter Notebook

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