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Showing posts from April, 2020

OCI Resource Manager (Terraform), Automating without Skills!

https://iaasgeek.com/2020/04/oci-resource-manager-terraform-automating-without-skills/ https://blogs.oracle.com/database/oracle-database-20c-preview https://www.martinberger.com/?p=5039     Oracle Cloud Infrastructure and SSH Keys – Jump!
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How to train a neural network on Chrome using tensorflow.js

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This tutorial is just a demonstration of how we can make use of simple scripting languages (like javascript). In this case, to train and predict using a neural network in the browser. We are going to use javascript. The main objective of this blog is to make use of a browser not only for using the internet but also for training a model behind the scenes. In this tutorial, we’re going to build a model that infers the relationship between two numbers where y = 2x -1 (y equals 2x minus 1). So let’s begin with our tutorial. Things we need for this tutorial 1. A simple HTML file containing a .js snippet. 2. A Google Chrome Browser. 3. A text editor to edit html file. Let’s start with creating a basic html file <!DOCTYPE html> <html> <head> <title>Training a model on browser</title> </head> <body> </body> </html> Now we need to import tensorflow.js library <script src=" https://cdn.jsdelivr.net/npm/@tensorflo
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Beginner’s Guide to Data Science Libraries in Python

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Data is what drives all industries today. From quantitative traders analyzing and forecasting stock market trends to healthcare professionals projecting the severity and virality of newborn pandemics, data is the key factor that is common to everything. Small amounts of data are easy to analyze with a calculator or an excel spreadsheet but once the calculations become complex and the amount of data exponentially grows, we need stronger tools under our belt. This is where software analytics and data science comes in. Although there are many available software analytic tools today, such as Matlab and R, the one I am focusing in today is Python. Python is very powerful when it comes to data analytics due to the multitude of libraries that many people have built over the years. Although it is important to thoroughly learn as many of these libraries as possible if you would want to pursue a career in data science, I will be going over some beginner libraries that people usually start
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Numpy Array Cookbook: Generating and Manipulating Arrays in Python

I once walked into a company completely unprepared as a data scientist. While I expected to be training models, my role turned out to be software engineering and the app made the heaviest use of numpy I’d ever seen. While I’d used  np.array()  to convert a list to an array many times, I wasn’t prepared for line after line of  linspace ,  meshgrid  and  vsplit . I needed to get comfortable with numpy fast if I was going to be able to read and write code. This is curated list of numpy array functions and examples I’ve built for myself. We’ll cover background info on Arrays in the first section, then get to the advanced functions that will help you become faster working with data. Table of Contents: 1. Array Overview 2. Generating Arrays 3. Manipulating Arrays 1) Array Overview What are Arrays? Array’s are a data structure for storing homogeneous data. That mean’s all elements are the same type. Numpy’s Array class is  ndarray , meaning “N-dimensional array”. i
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