Joseph Olaide's repositories
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.
examples
A repository to host extended examples and tutorials
Heruko-Airflow-Requisite
Contain requirements.txt and sample dag files for test reference.
mlops-zoomcamp
Free MLOps course from DataTalks.Club