josepholaide Goto Github PK

04-police-shootings

airqo-2022

aisaturday

axa-vehicle-insurance-claim

axa-vehicle-insurance-claim-katib

berttweet

data-engineering-with-python

Data Engineering with Python, published by Packt

datasets

A collection of datasets of ML problem solving

english2german-using-attention-and-lstm

examples

A repository to host extended examples and tutorials

financial_resilience

flower-species-classifier

gcp-projects

gender-based-violence-tweets-classifier

hamoye-works

heruko-airflow-requisite

Contain requirements.txt and sample dag files for test reference.

human-footprint-detector

indaba_water

iris_pytorchjob

josepholaide

katib-mavencode

kfaas

kubeflowtraining

machine-learning-oil-gas-industry

Source Code for 'Machine Learning in the Oil and Gas Industry' by Yogendra Narayan Pandey, Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, and Luigi Saputelli

manifests

A repository for Kustomize manifests

maven_kubeflow_reusable_pipeline

maven_pytorchjob

mlops-practice

mlops-zoomcamp

Free MLOps course from DataTalks.Club

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.