A curated list of AWS resources to prepare for the AWS Certifications
A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.
OP: @leonardofed founder @ plainflow.
| // create an IAM Lambda role with access to dynamodb | |
| // Launch Lambda in the same region as your dynamodb region | |
| // (here: us-east-1) | |
| // dynamodb table with hash key = user and range key = datetime | |
| console.log('Loading event'); | |
| var AWS = require('aws-sdk'); | |
| var dynamodb = new AWS.DynamoDB({apiVersion: '2012-08-10'}); | |
| exports.handler = function(event, context) { |
| ''' | |
| pairwise.py | |
| This script uses the Python Gensim library and heapq from the standard library to make | |
| massively fast and scalable pairwise comparisons between an aribtrarily large number of | |
| documents using TF-IDF and cosine distance. | |
| The script first generates a similarity matrix between all documents in a set, then uses | |
| heapq to retrieve the top K most similar matches to each document in that set. It has been | |
| tested on sets as large as 400,000 documents on a Macbook Air. |
Movies Recommendation:
Music Recommendation:
| I uploaded files modified from Shantnu Tiwari's Python For Engineers (PyEng) respository. | |
| Specifically, the Image_Video subdirectory has sample programs, which I modified to use matplotlib.pyplot's imshow() insead of OpenCV's imshow(). | |
| This is not ideal, but got the programs further along in the absense of a working OpenCV on Ubuntu. | |
| The next step was to avoid the GUI altogether and use OpenCV's imwrite() to save images to a file. The pyplot examples are left in, but commented out for reference. |
| # Clean workspace | |
| rm(list=ls()) | |
| # Load MXNet | |
| require(mxnet) | |
| # Loading data and set up | |
| #------------------------------------------------------------------------------- | |
| # Load train and test datasets |
