Year 2020 demanded big changes in our life style, especially in getting things done remotely. Networking and collaboration took a big hit due to this. The technology simply isn’t there yet to replace in-person human interactions for everyday tasks. One such area is the ability to express ideas freely on a whiteboard either during a meeting or for teaching. With just computer screens and the default hardware like mouse and keyboard a teacher cannot draw something easily to explain a concept better if it wasn’t clear from her slides. …
Web development has come a long way from static visualizations to complex single page apps (SPA), to the even more recent Progressive Web Apps (PWAs). SPAs allowed complex business logic in a web app and PWAs took it a step further by working without an active internet connection like native Android / iOS / desktop apps. In this blog, I want to show you how we can push the limits of a web app even further by training a Deep Convolutional Neural Network on GPUs completely in a browser and still maintain 60fps. …
In this post we are going to see three ways to affectively design the target classes in classification problems using the properties of the training data alone.
Classification is an area in Machine Learning (ML) where the machine is tasked to learn to categorize a given input. For example, given an image, the machine should return the category the image belongs to.
As shown in Figure 1 f is trained with labelled training images of cats and dogs. Here, “dog” is a category and “cat” is another category. These categories are also referred to as “classes” in classification problems. …
In this article I share the steps followed to successfully move WhatsApp data from an Android device to an iPhone. This process worked fine as of Feb 9, 2019 and costed $25 USD. It takes about an hour and heavily depends on the amount of data. And we do not need to root the android device. After reading several articles, I realized that there is no easy way to do this for free — not even for software developers! Wish WhatsApp provides a solution for this. If you know of a way please share in the comments.
ML algorithms work only with numerical values. So there is a need to model a problem and its data completely in numbers. For example, to run a clustering algorithm on a road network, representing the network / graph as an adjacency matrix is one way to model it.
Once transformed to numbers, clustering algorithms like k-means, to identify any underlying structure, can be easily invoked like so
from sklearn.cluster import KMeans
import numpy as npX = np.array([
[0, 1, 0],
[0, 0, 1],
[1, 1, 0]
])kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
Similarly, a tabular data with a mix of numerical and non-numerical / categorical data also needs to be transformed or encoded to a table of only numbers for a ML algorithm to work on. Columns of string values are quite common in tabular data and in this article, some ideas on how to encode them, especially ones with high cardinality and are of known lengths like IP addresses, mobile numbers etc. are discussed. How would or did you solve this problem? Please comment below. Would like to learn from your experiences as well…
Now, it is set up, login using psql -U postgres -h localhost or use PgAdmin for GUI.
By default user postgres will not have any login password.
Originally published at sites.google.com.
4. Login: psql -U postgres -h localhost -d gis
5. `CREATE EXTENSION postgis;`
6. Login to pgAdmin GUI software to check the functions and views added to this DB! These are all geospatial functions.
Originally published at sites.google.com.
This document is drafted from my experience after strenuous tinkering with multiple tutorials to create a tile server and a client that uses this tile server, basically after this you will have your own Maps, like Google does. This document lists high level steps to set up your tile server by pointing to existing excellent detailed tutorials for each step.
These are the major steps involved:
The server is responsible for serving tiles (nothing but PNG images) to any client requesting map information for a location. Each tile is 256 X 256 px in dimensions. A map has multiple zoom levels for every location, with 1 being birds eye view of the entire world and 18 being the deepest zoom level. So at zoom level 1, we have 1 tile to serve, for the entire world which is an image of 256 px by 256 px in size. If we zoom in to level 2, we have four tiles each again 256 px by 256 px. So every time we zoom in, we divide the tile from upper layer into 4 pieces. Based on client resolution and location requested, we return the corresponding tiles to the client. …