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Generative Adversarial Network

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Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.

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HROlive / Applications-of-AI-for-Anomaly-Detection

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Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.

python machine-learning deep-learning notebook tensorflow keras pandas generative-adversarial-network xgboost autoencoders anomaly-detection nvidia-gpu rapids
  • Updated May 23, 2024
  • Jupyter Notebook
Image-Generation-Using-GAN-Gen-AI-Project-

abhi227070 / Image-Generation-Using-GAN-Gen-AI-Project-

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Gen AI uses GANs to generate CIFAR-10-like images. The custom GAN model comprises a Generator and a Discriminator. Users can train the model and generate images using Jupyter Notebooks or Google Colab.

deep-neural-networks deep-learning generative-adversarial-network generative-model generative-art image-generation deeplearning generative-ai generative-ai-projects image-generation-ai
  • Updated Mar 30, 2024
  • Jupyter Notebook

ashsProjects / Data_Augmentation_Using_GANs

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This is a notebook exploring the implementation of a GAN using tensorflow in Python to generate additional data to augment the original training data for testing. The purpose of this project was to explore how the accuracies of different datasets differ with increasing samples. It uses a small dataset with ~600 samples and a larger one with ~10000.

tensorflow generative-adversarial-network gan classification
  • Updated Dec 6, 2023
  • Jupyter Notebook

Snigdho8869 / ML-DL-Projects

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A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow

nlp machine-learning computer-vision deep-learning clustering svm image-processing dnn lstm gru generative-adversarial-network generative-model image-classification convolutional-neural-networks transfer-learning image-segmentation cnn-model numerical-analysis regression-analysis random-forest-classifier
  • Updated Nov 30, 2023
  • Jupyter Notebook

Snigdho8869 / AI-Generative-Models-Notebooks-DCGAN-VAE-Autoencoder

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This repository contains notebooks showcasing various generative models, including DCGAN and VAE for anime face generation, an Autoencoder for converting photos to sketches, a captioning model using an attention mechanism for an image caption generator, and more.

tensorflow image-processing cnn generative-adversarial-network dcgan generative-model autoencoder vae generative-art image-captioning image-generation sketches variational-autoencoder generative-models upsample photo-to-sketch image-caption-generator deep-convolutional-gan anime-face-generation generative-ai
  • Updated Oct 31, 2023
  • Jupyter Notebook

SyedAanif / AI-ML-Python

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This repo contains learnings about Artificial Intelligence and Machine Learning using Python Jupyter Notebooks downloaded as HTML

python nlp machine-learning natural-language-processing reinforcement-learning ai computer-vision deep-learning linear-regression ml artificial-intelligence generative-adversarial-network neural-networks seq2seq artificial-neural-networks logistic-regression reinfrocement-learning tensorflow2 advanced-computer-vision
  • Updated Oct 24, 2023
  • Jupyter Notebook

sourcecode369 / unconventional-neural-networks

Star 11

Implementation notebooks and scripts of Artistic CNN Models and Generative Models like GANs, VAEs, GMMs, Boltzmann Machine etc. in TensorFlow, and Python. This repo aims to understand and make amazing things out of Neural Network layers.

python tensorflow keras pytorch recurrent-neural-networks generative-adversarial-network neural-networks autoencoder convolutional-neural-networks boltzmann-machines attention-mechanism variational-autoencoder generative-models neural-style-transfer
  • Updated Nov 22, 2022
  • Jupyter Notebook

Released June 10, 2014

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