Deep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.
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Updated
Apr 18, 2024 - Jupyter Notebook
Deep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.
K-CAI NEURAL API - Keras based neural network API that will allow you to create parameter-efficient, memory-efficient, flops-efficient multipath models with new layer types. There are plenty of examples and documentation.
Self learning of deep learning in several topics, also implementing them from scratch using Keras.
Contains courses in specializations of coursera on deep learning
Saves models in HDF5 FORMAT used in Keras and also TensorflowSavedModels
Seq2Seq model that restores punctuation on English input text.
Text Variational Autoencoder inspired by the paper 'Generating Sentences from a Continuous Space' Bowman et al. https://arxiv.org/abs/1511.06349
This repository gives informations how to implement deep learning algorithms with keras and pytorch libraries.
Open solution to the Cdiscount’s Image Classification Challenge
My experimentation around action recognition in videos. Contains Keras implementation for C3D network based on original paper "Learning Spatiotemporal Features with 3D Convolutional Networks", Tran et al. and it includes video processing pipelines coded using mPyPl package. Model is being benchmarked on popular UCF101 dataset and achieves result…
Utility for extracting layer weights and biases from Keras models
Keras callback function for stochastic weight averaging
Original Keras implementation of the code for the paper "Client-driven animated GIF generation framework using an acoustic feature," at 1171: Real-time 2D/3D Image Processing with Deep Learning (MTAP)
A beginners friendly guide for neural network. Implementing a random disease prediction under some given condition.
tensorflow2.x implementations of Generative Adversarial Networks.
A Capsule Network implementation in pure Keras running on Tensorflow 2.0
A Virtual Assistant for Windows PC with wicked Qt Graphics.
In this git you can find the dataset preprocessing and handling.
Control your PC's mouse by just looking at the screen!
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