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REVA University partners with CloudxLab for setting up Center of Excellence in AI and Deep Technologies

REVA University signs an MoU with CloudxLab to set up a Center of Excellence in AI and Deep Technologies: In the picture, Dr. K.M Babu, Vice Chancellor, Dr. Dhanamjaya, Pro-Vice Chancellor, Sandeep Giri, Founder, CloudX Labs.

REVA University’s REVA Academy for Corporate Excellence (RACE) has inked a memorandum of understanding with CloudxLab for setting up a Center of Excellence in AI and Deep Technologies and providing a platform for promoting research and innovation

REVA University and Cloudxlab research collaboration intends to work on technologies involving, deep learning, reinforced learning, curiosity-based machines, distributed computing, and launching specialized courses in these advanced technologies.

This collaboration will be aimed at providing and launching some highly sought-after courses in deep technologies involving experts from Academia as well as the industry. These courses will be delivered in hybrid mode – a combination of physical classroom, online instructor-led, self-paced, and project-based modes.

Dr. P. Shyama Raju, honorable Chancellor, REVA University said “This one-of-a-kind collaboration is aimed at being a launchpad for those who are planning to step into the world of AI, Deep Learning and other advanced technologies. It affirms REVA University’s commitment to make high-end technical education available to everyone in the world.”

“With Artificial Intelligence, machine learning, and other high-end technologies influencing every aspect of our lives, we are optimistic that this collaboration will help professionals in shaping their career”, says Sandeep Giri, CEO, and Founder at CloudxLab.

About REVA University

REVA University is one of the top-ranked private Universities in Bangalore, India, offering a wide range of UG, PG and PhD programs. REVA Academy for Corporate Excellence (RACE) is one of the initiatives of REVA University focused on corporate training to develop visionary enterprise leaders through progressive and integrated learning capabilities. RACE offers best-in-class, specialized, techno-functional, and interdisciplinary programs that are designed to suit the needs of working professionals.

Video processing with YOLOv4 and TensorFlow

In this blog we will show how to process video with YOLOv4 and tensorflow. YOLOv4 is out and it’s hot. YOLOv4 is significantly better than YOLOv3 as can be seen in the pic below.

Source

How to label custom images for YOLO – YOLO 3

In this blog we will show how to label custom images for making your own YOLO detector. We have other blogs that cover how to setup Yolo with Darknet, running object detection on images, videos and live CCTV streams. If you want to detect items not covered by the general model, you need custom training.

In our case we will build a truck type detector. There are 4 types of trucks we will try to identify

Setup Yolo with Darknet- Yolo 1

We will explore YOLO for image recognition in a series of blogs. This is the first one. In this blog, we will see how to setup YOLO with darknet and run it. We will also demonstrate the various choices you have with YOLO in terms of accuracy, speed and cost, enabling you to make a more informed choice of how you would want to run your models.

Setup Yolo with Darknet

The content in the blog is not unique. However if you are starting with YOLO, this is the first thing you need to do.

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.

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

import pandas as pd

Now, load the data into a DataFrame

dataframe = pd.read_csv('http://cowid.netlify.com/data/full_data.csv')

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)).

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.

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

def myfunc(x):
    if x < 30:
        mult = 10
    elif x < 60:
        mult = 20
    else:
        mult = 50
    return x*mult