This post walks you through how to set up a simple Python app where one can upload data, triggering a script to read the uploaded data and process it, and download the results in the end.

Oracle is a popular DBMS and this post walks you through how to set up the connectivity between it and Python.

Microsoft’s Power Automate is a great tool to automate certain processes and workflows without requiring advanced coding skills. This post shows you an example of how we can leverage Power Automate in a real-life business task.

Power BI has an embedding capability that allows developers to embed a report, dashboard or tile in a web page. Viewers no longer need a license to access the reports or dashboards. This post shows you how to set the process up.

Without doubt, PHP’s main use is web development. However, we can apply it to generate some highly customizable PDFs. This post walks you through the entire process, from initial setup to the final product. 

I implemented the algorithm in Python and the code is available on my GitHub site in Jupyter Notebook format. I hope my solution serves as a starting point for anyone who is trying to solve such problems. 

We want to assign 6 employees to 30 projects while maximizing total client satisfaction. As long as a project is completed by the deadline, its starting date is flexible. No employee can be on multiple projects at the same time.

I came across a good article in Journal of Economic Perspectives on big data: Machine Learning: An Applied Econometric Approach.

This post goes through the last 2 examples of DLP Ch 8. They demonstrate how image generation in DL works and the approach can be extended to sound and text.

Ch 8 is the last chapter with practical applications in Deep Learning with Python. It includes 5 examples to showcase how DL can be used for creativity purposes. This post goes over the first 3 examples.

Ch 7 in Deep Learning with Python goes through some of the other key functionalities offered by Keras library. 

Ch 6 in Deep Learning with Python deals with sequence data such as text (sequence of words) and time series.

Ch 5 in Deep Learning with Python covers the topic of computer vision. It teaches us how to build DL models that can classify pictures as cats or dogs.

Ch 4 in Deep Learning with Python touches on 3 fundamental concepts in machine learning: 4 branches of ML, evaluation of ML models, and regularization.

Ch 3 in Deep Learning with Python (DLP) includes 3 examples: Classify IMDb movie reviews as positive or negative, Classify Reuters newswires into 46 different topics, and Predict median home prices in suburbs of Boston.

As mentioned in the last post, we will run all our DL exercises on GPU. It requires us to set up a separate virtual environment where our Python programs will be run on GPU instead of CPU. Also, we will use Keras library.

There are so many buzzwords in the field these days: big data, machine learning, deep learning, artificial intelligence, etc. How are they all related? Deep learning is simply one of the subfields in machine learning.

R and Python solutions to all the Applied Exercise questions in An Introduction to Statistical Learning with Applications in R are available on my GitHub site.

the key points I summarized from Chapters 9 and 10 in An Introduction to Statistical Learning with Applications in R

the key points I summarized from Chapters 7 and 8 in An Introduction to Statistical Learning with Applications in R

the key points I summarized from Chapters 5 and 6 in An Introduction to Statistical Learning with Applications in R

the key points I summarized from Chapters 1-4 in An Introduction to Statistical Learning with Applications in R