Is there a better time than a Lock-down to Up-skill yourself?

As the world comes to a screeching halt due to the Covid-19 pandemic, it would be reasonable to suggest that there isn’t much to look forward to in the immediate future. As we lay within the confines of our homes, we realize how little it takes to upset the established order.

Continue reading “Is there a better time than a Lock-down to Up-skill yourself?”

Analyzing data of the global pandemic – COVID – 19 using Python

Covid-19 has been declared as a global pandemic. If you are interested to analyse the data by yourself, please follow the instructions below.

The data is made available to everyone at the locations given below.

The data files are in the form of four metrics and are available to download here:
Total confirmed cases:https://cowid.netlify.com/data/total_cases.csv
Total deaths:https://cowid.netlify.com/data/total_deaths.csv
New confirmed cases:https://cowid.netlify.com/data/new_cases.csv
New deaths:https://cowid.netlify.com/data/new_deaths.csv
The full dataset is available to download here:http://cowid.netlify.com/data/full_data.csv

In your Jupyter notebook, please follow the instructions given below. To avoid installations of Jupyter or any library, you can simply use CloudxLab – it gives 15 days free subscription.

Import the Pandas library

Now, load the data into a DataFrame

Once the data has been loaded, you can perform various operations on it. Please see the image attached below to get an idea of the entire process.

The code for this is available here: https://github.com/cloudxlab/covid19/blob/master/Covid-19.ipynb

CloudXLab is proud to sponsor RACE360 as a Technology Partner.

RACE360, an Emerging Technology Conference 2019 (Powered by The Times of India) is happening on Wed, Aug 28th at The Lalit Ashok, Bengaluru. It is presented by REVA University, Bengaluru (REVA Academy for Corporate Excellence (RACE)).

Continue reading “CloudXLab is proud to sponsor RACE360 as a Technology Partner.”

Conference on Computer Vision at Google Asia, Singapore

The deep learning algorithms and frameworks have changed the approach to computer vision entirely. With the recent development in computer vision with Convolutional Neural Networks such as Yolo, a new era has begun. It would open doors to new industries as well as personal applications.

After the successful bootcamps held at IIT Bombay, NUS Singapore, RV College of Engineering, etc, CloudxLab in collaboration with IoTSG and Google Asia conducted a successful conference on Understanding Computer Vision with AI using Tensorflow on May 11, 2019, at Google Asia, Singapore office.

Continue reading “Conference on Computer Vision at Google Asia, Singapore”

Regression with Neural Networks using TensorFlow Keras API

As part of this blog post, I am going to walk you through how an Artificial Neural Network figures out a complex relationship in data by itself without much of our hand-holding. You should modify the data generation function and observe if it is able to predict the result correctly. I am going to use the Keras API of TensorFlow. Keras API makes it really easy to create Deep Learning models.

Machine learning is about computer figuring out relationships in data by itself as opposed to programmers figuring out and writing code/rules. Machine learning generally is categorized into two types: Supervised and Unsupervised. In supervised, we have the supervision available. And supervised learning is further classified into Regression and Classification. In classification, we have training data with features and labels and the machine should learn from this training data on how to label a record. In regression, the computer/machine should be able to predict a value – mostly numeric. An example of Regression is predicting the salary of a person based on various attributes: age, years of experience, the domain of expertise, gender.

The notebook having all the code is available here on GitHub as part of cloudxlab repository at the location deep_learning/tensorflow_keras_regression.ipynb . I am going to walk you through the code from this notebook here.

Generate Data: Here we are going to generate some data using our own function. This function is a non-linear function and a usual line fitting may not work for such a function

Continue reading “Regression with Neural Networks using TensorFlow Keras API”