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Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
Projeto final de conclusão da disciplina de Visão Computacional voltado a auditoria de vídeo e construção de classificadores baseado em transfer learning.
An Image Classifier web app built with Fastai and Pytorch using Transfer Learning. These notebooks contain the code from the medium blog articles from the series: A Fast Introduction to Fastai-My Experience.
This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.
An IPython notebook demonstrating the process of Transfer Learning using pre-trained Convolutional Neural Networks with Keras on the popular CIFAR-10 Image Classification dataset.
This project shows step-by-step guide on how to build a real-world flower classifier of 102 flower types using TensorFlow, Amazon SageMaker, Docker and Python in a Jupyter Notebook.
Code and notebook for text summarization with BERT along with a simple baseline model. Includes a research-backed treatment on the state of transfer learning, pretrained models, NLP metrics, and summarization dataset resources.