GitHub - Frightera/Sample-Machine-Learning-Projects: Some example projects that was made using Tensorflow (mostly). This repository contains the projects that I've experimented-tried when I was new in Deep Learning.

Project #	Network Type	Project
1	LSTM + W2C (Keras)	Sentiment Analysis
2	Generative Adversarial Network	Generate Synthetic Images
3	LSTM Autoencoder	Detect Anomalies in Time Series
4	Siamese Network	MNIST Handwritten Digits
5	Autoencoders	Reducing Dimensions In Data
6	Convolutional Neural Networks	COVID-19 Detection Using X-Rays
7	SARIMAX + FB Prophet	COVID-19 Time Series
8	Random Forest Classifier	Imbalanced CC Fraud Detection
9	Ensemble ( VGG19 + Xception )	Malaria Dataset Classification
10	NLP TF Hub Models	NLP Transfer Learning
11	Simple Probabilistic Model	HAR Dataset Weights & Outputs Uncertainty
12	Plain FC NN	From Scratch in TF 2.x
13	BERT (Pytorch)	Sentiment Analysis Twitter SMILE dataset
14	Autoencoder	Image Denoising
15	2D Convolutional NN	Visualizing Filters of CNNs
16	ResNet152 Transfer Learning	CIFAR10 Dataset & 96% Accuracy on Test Set

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
2D Convolution Filters		2D Convolution Filters
Autoencoder - Image Denoising		Autoencoder - Image Denoising
Autoencoder Dimensionality Reduction v2		Autoencoder Dimensionality Reduction v2
CC Fraud Detection Imbalanced Data		CC Fraud Detection Imbalanced Data
COVID-19 Prediction Chest X-Ray Images		COVID-19 Prediction Chest X-Ray Images
COVID-19 Time Series		COVID-19 Time Series
DCGAN Image Generation		DCGAN Image Generation
Deep Ensemble CNN		Deep Ensemble CNN
Fake News Detection LSTM + Embedding		Fake News Detection LSTM + Embedding
NLP - Bert (Pytorch)		NLP - Bert (Pytorch)
NLP-Tensorflow Hub		NLP-Tensorflow Hub
NN from Scratch TF 2.x		NN from Scratch TF 2.x
Power of Transfer Learning CIFAR10 96%		Power of Transfer Learning CIFAR10 96%
Siamese Network - MNIST		Siamese Network - MNIST
Time Series Anomaly Detection		Time Series Anomaly Detection
Weights & Outputs uncertainity		Weights & Outputs uncertainity
README.md		README.md

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