Amazon Web Services
◦ Amazon DynamoDB - 25GB NoSQL DB
◦ Amazon Lambda - 1 Million requests per month
◦ Amazon SNS - 1 million publishes per month
◦ Amazon Cloudwatch - 10 custom metrics and 10 alarms
◦ Amazon Glacier - 10GB long-term object storage
◦ Amazon SQS - 1 million messaging queue requests
◦ Amazon CodeBuild - 100min of build time per month
◦ Amazon Code Commit - 5 active users per month
◦ Amazon Code Pipeline - 1 active pipeline per month

IBM Cloud
◦ Cloud Functions - 5 million executions per month
◦ Object Storage - 25GB per month
◦ Cloudant database - 1 GB of data storage
◦ Db2 database - 100MB of data storage
◦ API Connect - 50,000 API calls per month
◦ Availability Monitoring - 3 million data points per month
◦ Log Analysis - 500MB of daily log

Oracle Cloud
◦ Compute - 2 VM.Standard.E2.1.Micro 1GB RAM
◦ Block Volume - 2 volumes, 100 GB total (used for compute)
◦ Object Storage - 10 GB
◦ Load balancer - 1 instance with 10 Mbps
◦ Databases - 2 DBs, 20 GB each
◦ Monitoring - 500 million ingestion datapoints, 1 billion retrieval datapoints
◦ Bandwidth - 10TB egress per month
◦ Notifications - 1 million delivery options per month, 1000 emails sent per month

PyTorch 1.8 Release with native AMD support!

- 611 datasets you can download in one line of python
- 467 languages covered, 99 with at least 10 datasets
- efficient pre-processing to free you from memory constraints

https://github.com/huggingface/datasets

Q: Shall we remove the duplicate records (i.e. records with exactly the same features) from the dataset before training an ML model?

A: It depends. If the duplicated records belong to a single instance/event (e.g. when one instance is captured twice), they should be removed. For example, by looking at the customer_IDs, we may notice some of the customers are duplicated in our data. In this case, we should deduplicate. Otherwise, the ML model cannot estimate the prior probability distribution correctly.

On the other hand, if the records with the same features belong to different instances/events, we should keep them. For example, if two customers have the same age, sex, balance, and etc, their data should be used to train the model.

To have a better understanding, consider a Naive Bayes model for a classification problem. By removing the samples with the same features, the model misestimates the prior probabilities that eventually affects the output.

Intuitively, the model needs to know the frequency/distribution of those duplicated records.

Automated workplace #ergonomics assessment using motion capture to remove risk factors that lead to musculoskeletal injuries (#MSD) and to help human performance and #productivity. Ergo simulation software by Nawo Solution & Pierre FOUBERT Wilo Group

Stats for Datascience and Machine Learning

Actually, here are some sites where I have found some of the highest quality, free machine learning educational content:

🔹GitHub
🔹Kaggle
🔹Coursera
🔹YouTube
🔹Papers with Code
🔹fast.ai
🔹PyImageSearch
🔹Machine Learning Mastery
🔹Wikipedia

In the last 10 years, AI-related PhDs have gone from 14.2% of the total of CS PhDs granted in the U.S.  to around 23% as of 2019, according to the CRA survey. At the same time, other previously popular CS PhDs have declined in popularity, including networking, software engineering, and programming

GenoML: Automated Machine Learning for Genomics
pdf: arxiv.org/pdf/2103.03221…
abs: arxiv.org/abs/2103.03221
project page: genoml.com

How to Automate Exploratory Data Analysis (EDA) ? - Part 1 https://youtu.be/tMquUTJ6yXU


You should know when you want to expedite data analysis 🧐 I strongly recommend you to use in your real world problems. This module will help you a lot

This website will help you learn probability and statistics, the most important topics in math for machine learning!

seeing-theory.brown.edu


Don’t forget to add in bookmarks 🔖

Deep learning activation functions made cool and cute