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cats-and-dogs-data-prepration

deep learning data prepration

class.vision

Computer vision and Deep learning in SRU

clustering-kmeans

data-engineering-zoomcamp

Free Data Engineering course!

data_pipeline

Sample data pipeline using Kafka, Spark, Airflow

dataminig

deep-learning-

Hodadataset with Keras

deep-learning-for-natural-language-processing

Solve your natural language processing problems with smart deep neural networks

deep-learning-image-processing-numbers-dataset

deep-learning-neural-networks

Hoda dataset solved with keras algorithms(neural network)

deepnanodesign

eda-preprocessing2

null removal diomentional reduction and augmentation

edapreprocessing1

edvr

Winning Solution in NTIRE19 Challenges on Video Restoration and Enhancement (CVPR19 Workshops) - Video Restoration with Enhanced Deformable Convolutional Networks. EDVR has been merged into BasicSR and this repo is a mirror of BasicSR.

essential-sqlalchemy-2e

Sqlalchamy

image-preprocessing

image-to-array

incoherent-light-simulation

Research simulation of the propagation of incoherent light, aiming to illustrate the concept of spatial coherence.

jalali-pandas

shamsi dates really works

jalali.py

Persian and Gregorian Date Converter for Python 2 and Python 3

jupydocker

Repo for How and Why to Put Jupyter Notebooks into Docker Containers (PyData 2019)

jupyter-x-docker-on-heroku

Build a Python Jupyter Notebook Server with Docker & Heroku.

ml_pcf

Multilayer perceptron has been implemented using PyTorch framework to compute various optical properties of a photonic crystal fiber (PCF).

multiclass_metasurface_inversedesign

Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548

photontorch

Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch

pneumonia-diagnosis-using-xrays-96-percent-recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

quantum-learning

This repository contains the source code used to produce the results presented in the paper "Machine learning method for state preparation and gate synthesis on photonic quantum computers".

scatternet

Code for all work presented in Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks

sru-deeplearning-workshop

دوره 12 ساعته یادگیری عمیق با چارچوب Keras

stockmarket_prediction