Author Archives: Ajitesh Kumar
Niramai uses AI / Thermal Imaging for Breast Cancer Screening
Niramai Health Analytix, a Bengaluru-based startup is creating an AI-powered software system for breast cancer screening. Niramai is using following technologies to achieve the objective of breast cancer screening: Thermal image processing using thermal sensing device (thermal camera) Machine learning algorithm Hardware devices integrated with real-time cloud-based diagnostics; These hardware devices are capable of capturing thermal images What/How of Thermal Image Processing? Thermal image processing, also termed as thermal imaging, is a method of improving visibility of objects in a dark environment by detecting the objects’ infrared radiation and creating an image based on that information. source: techtarget. The key to capturing thermal images of an object is a heat sensor (also called as thermal camera) which is …
AIndra uses AI to Solve Cervical Carcinoma Cancer
AIndra, a Bengaluru-based startup is into the business of building innovative products and technologies to aid computational pathology. Established in 2014, AIndra’s vision is to build state of the art medical devices for screening Cervical Carcinoma. This article is created solely based on my analysis of information found on AIndra’s website. The objective is to make readers aware of some of the following technologies AIndra is using and, how they can be used to solve healthcare problems, in general. The goal is provide food for thought to the readers such that they can use some of these technologies in their future startups. Computation Pathology Telepathology If you work in AIndra, please feel …
15+ AI Startups for Cancer Prediction / Treatment
This is a list of 15+ startups using artificial intelligence (AI) technologies for cancer/oncology prediction / treatment. Please feel free to suggest if any piece of information given below is incorrect or incomplete. This page will be updated from time-to-time. Startup Name Field of Interest Startup Brief OuroTech Cancer treatment Help doctors identify in advance which drugs are most likely to have the biggest impact killing a type of cancer. Doctor Hazel Skin Cancer Helps predict whether you are having from Skin cancer Color Genomics Genetic testing Focus on testing for mutations leading to a higher risk of certain cancers. Mendel.ai Clinical trials AI to match cancer patients with the latest clinical trials Grail Cancer …
Tutorials – Building Machine Learning Models for Predicting Cancer
In this article, I would introduce different aspects of the building machine learning models to predict whether a person is suffering from malignant or benign cancer while emphasizing on how machine learning can be used (predictive analysis) to predict cancer disease, say, Mesothelioma Cancer. The approach such as below can as well be applied to any other diseases including different types of cancers. Predicting Mesothelioma Cancer – Supervised Learning Problem Machine learning problems are classified into different kinds of learning problem. Most important of them are following: Supervised learning Unsupervised learning Supervised Learning In supervised learning, you have a history of data with each record being labeled. Thus, in case of predictive analysis of Mesothelioma cancer, there is …
Neural Networks Interview Questions – Set 2
This quiz represents practice test on artificial neural networks. These questions and answers can be as well used for your upcoming interviews for the position of machine learning engineer or data scientist. These questions can prove to be very useful for testing your neural networks knowledge from time-to-time. Also, these will be useful for interns / freshers / beginners of machine learning / data science. The topics covered in this practice test are following: Introduction to different types of neural networks such as Radial Basis Network, Recurrent neural network etc. Difference between multilayer perceptron (MLP) and Radial basis function network Practice Test on Neural Networks [wp_quiz id=”6000″]
AWS Cloud9 IDE and Java / PHP Hello World
This article represents tutorial in relation to how to get started with creating your first Java / PHP Hello World program using AWS Cloud9 IDE. Supported Runtimes in AWS Cloud9 Before getting started with creating runtime environment and execute hello world programs in Java and PHP and other languages, lets look at what all runtimes are supported. Following screenshot represents the supported runtime: The above represents the fact that one could create programs / application using one of the following programming languages: C C++ Java Go Node.js PHP Python Ruby In this article, I have shown how to create hello world program using Java and PHP programming language. The following steps represent way to create your hello world programs. The following are some of the steps …
Top 10 Tricky Interview Questions for Java Developers
Here is a list of top ten (10) tricky / popular interview questions and answers for Java developers. I got these questions out from Stackoverflow. You are a Junior or Intermidiate level Java developer and planning to appear for Java developer interviews in near future, you would find these questions to be useful enough. Q1: Is Java “pass-by-reference” or “pass-by-value”? Ans: Java is always “pass by value”. Read the details on this page, Is Java “pass-by-reference” or “pass-by-value”? Q2: How to create a memory leak in Java? Ans: This is possible by making use of Class loader and ThreadLocal. Read the details on this page, Creating a memory leak in Java Q3: What is difference between package private, public, protected, and private? Ans: A private member …
K-Means Clustering Interview Questions – Set 1
This is a practice test on K-Means Clustering algorithm which is one of the most widely used clustering algorithm used to solve problems related with unsupervised learning. This can prove to be helpful and useful for machine learning interns / freshers / beginners planning to appear in upcoming machine learning interviews. This practice tests consists of interview questions and answers in relation with following: Introduction to K-Means Clustering Cost function Practice Test on K-Means Clustering [wp_quiz id=”5961″]
Why use AWS Fargate for Deploying Your Cloud-Native Apps
In past, if you have been used to deploying your cloud-native apps or microservices hosted within a Docker container on AWS ECS, you may have also been required to do some of the following: Choose server types in relation with provisioning AWS EC2 instances Configure appropriately to scale the cluster as and when required Optimize cluster packing In short, with AWS ECS, until the launch of AWS Fargate, you were required to manage the infrastructure and related configuration in relation with scalability etc. With AWS Fargate, Focus in on Designing and Building Apps With AWS Fargate, all that is required to be done are some of the following: Package your application in containers Specify the CPU and memory requirements Define …
AWS EKS is the Way to Run Kubernetes on AWS ECS
AWS Elastic Container Service for Kubernetes, AWS EKS, is a new AWS fully managed service running Kubernetes out-of-box on AWS without needing to install and operate / manage our own Kubernetes clusters. . Gone are the days when we (63% of Kubernetes workloads as per CNCF spent time and effort setting up and running / managing Kubernetes (Master and a cluster of workers) on AWS EC2 instances with no support from AWS service. Not only this, there was whole lot of high availability requirements in relation with Kubernetes which needed to be managed by running Kubernetes master on different availability zones (AZs). Key Features of AWS EKS Support for existing plugins and …
Free Books / Lecture Notes on Quantum Computing
This page represents a list of popular / top / free books and lecture notes on Quantum Computing. This page will be updated from time-to-time. Please feel free to suggest any good books on Quantum Computing that can be added to the list given below: Quantum Computation and Quantum Information: This book is authored by Michael A. Nielsen, Isaac L. Chuang. Interestingly, Michael A. Nielsen has also a free online book on Neural Network and Deep Learning. Worth a visit on his pages. Classical and Quantum Computation by Alexei Yu. Kitaev, Alexander Shen, Mikhail N. Vyalyi. Here is a page listing down work of Alexei Kitaev on Quantum Computing. Here is a page listing down other work of Mikhail Vyalyi. Quantum …
Dummies – How to Start Learning Quantum Computing
If you are a beginner / rookie / fresher to quantum computing and wondering on how to get started with Quantum Computing, here is the great piece of advice on learning Quantum Computing, posted by Aram Harrow, assistant professor of Physics at MIT. The following is the summary: Quantum computing is at the intersection of Mathematics, Physics and Computer Science. Given above information, get started with learning some of the following: Physics (Quantum mechanics) Mathematics (Linear Algebra and Probability). Other topics may include group and representation theory, random matrix theory and functional analysis. Computer Science topics including but not limited to algorithms, cryptography, information theory, error-correcting codes, optimization, complexity, machine learning Quantum Computing Bookmarks You may bookmark some …
Top 12 US Universities for Quantum Computing Research
Here is the list of top 12 US Universities where quantum computing research is going on. This list will be updated from time-to-time. Please feel free to suggest. MIT – Quantum Information and Integrated Nanosystems: This group is doing applied research and prototype demonstrations that draw upon a broad foundation of innovative device design, outstanding fabrication tools, and well-equipped measurement infrastructure. University of Berkeley – Berkeley Quantum Information & Computation Center University of Chicago – Chicago Quantum Exchange: The following are some of the research areas: Condensed Matter Physics Atomic, Molecular, and Optical Physics Physical Chemistry Quantum Information Quantum Optics Quantum Sensing Nanomechanics Topological Physics Device Physics UC Santa Barbara: The following is a list …
Top 8 Neural Networks and Deep Learning Tutorials
Here is a list of top 8 neural networks tutorials (web pages) for getting started on neural networks and deep learning. Introduction to Deep Neural Networks Neural Networks and Deep Learning: Free online book to learn concepts related with neural networks and deep learning. Very good for beginners. Concepts explained using Handwritten digits. The book is authored by Michael Nielsen. Neural Networks: The page explains and demonstrates various types of neural networks along with applications of neural networks like ANNs in medicine. Coursera Course on Neural Networks for Machine Learning: This can be used to learn fundamentals related with artificial neural networks and how they’re being used for machine learning, …
Kubernetes Interview Questions and Answers – Set 1
This page represents practice test consisting of objective questions on Kubernetes. The practice test can prove to be very helpful if you are preparing to take Certified Kubernetes Administrator (CKA) certification examination in near future. It covers the Core Concepts from CKA certification exam syllabus. Those preparing for interviews in relation with Kubernetes or cloud-native apps would find these questions to be useful enough. These questions can prove to be useful for interns / freshers / beginners. These questions are related with some of the following topics: Introduction to Kubernetes Kubernetes objects Kubernetes controllers Below are other practice tests on Kubernetes concepts such as Pods, Pods lifecycle, Container hooks lifecycle, Kubernetes 1.8 release concepts such as …
20+ Usecases in Quantum Computing
Here is a list of 20+ Quantum Computing Use Cases in some of the following fields: Machine Learning and Computer Science Financial Modeling Healthcare and medicine Security and mission planning Machine Learning and Computer Science Detecting statistical anomalies Finding compressed models Recognizing images and patterns Training neural networks Verifying and validating software Classifying unstructured data Diagnosing circuit faults Financial Modeling Detecting market instabilities Developing trading strategies Optimizing trading trajectories Optimizing asset pricing and hedging Optimizing portfolios Healthcare and Medicine Detecting fraud Generating targeted cancer drug therapies Optimizing radiotherapy treatments Creating protein models Security and Mission Planning Detecting computer viruses & network intrusion Scheduling resources and optimal paths Determining set membership …
I found it very helpful. However the differences are not too understandable for me