This repository is related to the paper "Data augmentation and hierarchical classification to support the diagnosis of neuropathies based on time series analysis".

Build a custom convolutional neural network neural network from scratch in Tensorflow to identify the type of skin cancer from image.

This application is made to perform data augmentation with an easy to use interface. You are provided with various traditional data augmentation techniques like rotation, crop, zooming, etc.

Data augmentation is a technique used to create more examples, artificially, from an existing dataset. This is useful if the dataset is small and we want to increase the number of examples. Data augmentation can often solve over-fitting so that our model generalizes well after training. For images, a variety of augmentation can be applied to incr…

L'objectif de ce projet est de développer un outil d' aide à la décision pour les banques permettant de vérifier l’authenticité de la signature d'identifier les chèques falsifiés.

Simple numpy-based implementation of SpecAugment

This repository presents a gemstone classification project employing Transfer Learning with MobileNetV2, processing a dataset comprising 3200+ images spanning 87 classes. TensorFlow and Keras facilitated data preprocessing, augmentation, and model training. Through fine-tuning and leveraging pre-trained features.

Flower Classification with CNN & Data Augmentation

A repository for our research study "SC-Beta-VAEGAN"

Face Mask Detection Competition on Kaggle

Curso Convolutional Neural Networks in TensorFlow disponibilizado pela DeepLearning.AI e ministrado através do Coursera

Disease - Symptom Dataset Cleaning and Augmenting Process

Brain tumor detection using Deep learning in MRI images

Notebook for Data Augmentation

Using Convolutional Neural Networks to create image classifiers [ cats + dogs & cats + dogs + lions + tigers ]

MSc thesis project: classification of italian certified electronic mails using SOTA machine learning, fine-tuning pre-trained deep learning models and data augmentation techniques