Pinned Repositories
04-police-shootings
AirQo-2022
AXA-Vehicle-Insurance-Claim
examples
A repository to host extended examples and tutorials
Financial_Resilience
Josepholaide
KfaaS
MLOps-Practice
Natural-Scene-Digits-Identifier
For the capstone project, you will use the SVHN dataset. This is an image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. "Reading Digits in Natural Images with Unsupervised Feature Learning". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. The train and test datasets required for this project can be downloaded from here and here. Once unzipped, you will have two files: train_32x32.mat and test_32x32.mat. You should store these files in Drive for use in this Colab notebook. Your goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes.
Volve-Energy-ML
josepholaide's Repositories
josepholaide/AXA-Vehicle-Insurance-Claim
josepholaide/MLOps-Practice
josepholaide/Volve-Energy-ML
josepholaide/Natural-Scene-Digits-Identifier
For the capstone project, you will use the SVHN dataset. This is an image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. "Reading Digits in Natural Images with Unsupervised Feature Learning". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. The train and test datasets required for this project can be downloaded from here and here. Once unzipped, you will have two files: train_32x32.mat and test_32x32.mat. You should store these files in Drive for use in this Colab notebook. Your goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes.
josepholaide/04-police-shootings
josepholaide/AirQo-2022
josepholaide/examples
A repository to host extended examples and tutorials
josepholaide/Financial_Resilience
josepholaide/Josepholaide
josepholaide/KfaaS
josepholaide/AXA-Vehicle-Insurance-Claim-Katib
josepholaide/BertTweet
josepholaide/English2German-using-Attention-and-LSTM
josepholaide/Flower-species-classifier
josepholaide/GCP-Projects
josepholaide/Gender-Based-Violence-Tweets-Classifier
josepholaide/Heruko-Airflow-Requisite
Contain requirements.txt and sample dag files for test reference.
josepholaide/Human-footprint-detector
josepholaide/Indaba_Water
josepholaide/Iris_pytorchjob
josepholaide/Maven_kubeflow_reusable_pipeline
josepholaide/Maven_pytorchjob
josepholaide/mlops-zoomcamp
Free MLOps course from DataTalks.Club
josepholaide/News-Classification
josepholaide/NLP-in-Fiction
josepholaide/Police-Shooting
josepholaide/Realtor-Analysis
josepholaide/Sendy-Logistics
josepholaide/Titanic-katib
josepholaide/Wine-tasting