fitrialif

-Convolutional-Neural-Network

This is a convolutional neural network trained with more than 15,000 dogs and cat images respectively. I have built this network three layers deep, giving an accuracy of 86%.

-Sci-Kit-Learn-Image-Classification---Predicting-Digits---Stocastic-Gradient-Descent-SGD-

1. Preprocessing data, creating a simple stocastic gradient descent binary classifier and performing K-fold Cross Validation.

100-Days-Of-ML-Code

100 Days of ML Coding

2016_Facial-Expression-Recognition

Age-Gender-Emotion-Mobilenet

Transfer learning for age, gender and emotion classifications on mobilenet architecture in a single feed-forward!

CompactCNN_for_FER

A Pytorch implementation of "A Compact Deep Learning Model for Robust Facial Expression Recognition (CVPR 2018)".

DL-project

Improve CNN model using data augmentation with GAN

facial-expression-recognition-keras-FER2013

This code is using FER2013 dataset with keras library and tensorflow backend. This code was fork and modified for keras with tensorflow backend from https://github.com/LamUong/FacialExpressionRecognition

keras_ocr_letter

中文OCR单字识别 keras实现

make_pytorch_dset_from_EEG

code to make topography image datasets for pytorch from EEG features (*.mat)

fitrialif/DL-project

Improve CNN model using data augmentation with GAN

fitrialif/CNN-with-GA-Keras

2-layer Convolutional Neural Network with Genetic Algorithm (GA) implementation.

fitrialif/CoDeepNEAT

An implementation of CoDeepNEAT with possible extensions

fitrialif/computervision-recipes

Best Practices, code samples, and documentation for Computer Vision.

fitrialif/Deep-Learning-1

fitrialif/EEG_emotion

EEG sentiment analysis

fitrialif/eval-nas

Code for "Evaluating the search phase of neural architecture search"

fitrialif/Evolution-1

Genetic algorithm applied to a basic neural network in keras using DEAP (https://github.com/DEAP/deap)

fitrialif/Evolving-CNNs-using-GA

Evolving Architectures for Convolutional Neural Networks using the Genetic Algorithm

fitrialif/evoNAS

Repo for 3rd research project: Evolutionary Neural Architecture Search

fitrialif/face-emotion-recognition-dpn-keras

fitrialif/GARNE-Genetic-Algorithm-with-Recurrent-Network-and-Novelty-Exploration

fitrialif/General-Advanced-Deep-Learning-Trainings

Contents, •Neural networks – Perceptron, Adaline, BP neural networks, unsupervised learning neural networks, RBF neural networks, etc. •Optimization methods – Genetic algorithms, swarm intelligence, etc. •Training deep neural networks – Parameter and structure tuning, etc. •Deep learning neural network models – Convolutional Neural Networks (CNN), autoencoders

fitrialif/Genetic_Evolution_For_CNN

fitrialif/GeneticCNN

fitrialif/Kaggle_digit_recognizer_with_deap

Kaggle digit recognizer with deap is an implementation of genetic algorithm that tries to optimize model produced by lenet5 using this CNN option (learning rate, batch size, receptive field etc) as hyperparameters optimized by genetic algo

fitrialif/Keras-CoDeepNEAT-1

CoDeepNEAT inspired implementation using Keras and Tensorflow as backend.

fitrialif/keras-idiomatic-programmer

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

fitrialif/keras-ocr

A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.

fitrialif/KerasEvolution-1

NEAT-like genetic algorithm for evolving best Neural Networks for a given dataset

fitrialif/neat-python

A Python version of the NEAT algorithm (NeuroEvolution of Augmenting Topologies)

fitrialif/Neural-Architecture-Search-GA

A basic Neural Architecture Search using Multi Objective Genetic Algorithms. Hyper-parameter tuning with Genetic Algorithms. Done as part of the Artificial Intelligence course at IIIT-D

fitrialif/NeuroEvolution-with-Keras

fitrialif/nsga-keras

evolution for NAS based on NSGA/NSGAII/NSGAIII (with parallel evaluation)

fitrialif/object-detection-in-keras

fitrialif/petridishnn

Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search

fitrialif/ResearchChatGPT

50 use cases of ChatGPT for research work

fitrialif/SMate--SyntheticMinorityAdversarialTechnique

The novel SMate approach leverages GAN minority-class image generators, which benefit from Transfer Learning from majority-class image generators. Consequently, SMate outperforms SMOTE for imbalanced image data-sets. Research at Stanford University, by: Pablo Rodriguez Bertorello, Liang Ping Koh

fitrialif/Traffic-Signal-Violation-Detection-System

A Computer Vision based Traffic Signal Violation Detection System from video footage using YOLOv3 & Tkinter. (GUI Included)

fitrialif/VGG16-In-Keras

Implementation of VGG16 architecture in Keras