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All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]


Online Courses Udemy - All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python], Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence Created by Rishi Bansal English Students also bought Java from Zero to First Job: Part 1 - Java Basics and OOP C Programming for Beginners - Master the C Fundamentals Full-Stack Web Development For Beginners The Complete Java Programmer: From Scratch to Advanced Python and Django Full-Stack Web Development for beginners Learn To Create AI Assistant (JARVIS) With Python Preview this course GET COUPON CODE Description This course is designed to cover maximum Concept of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. As a Bonus Introduction Natural Language Processing and Deep Learning is included. Below Topics are covered Chapter - Introduction to Machine Learning - Machine Learning?

Beyond triplet loss : One shot learning experiments with quadruplet loss


This article is a follow up to my previous article about One Shot learning, Siamese networks and Triplet Loss with Keras. "One Shot Learning" and "Mining" are described there, so if you're not familiar with these concepts yet, I highly recommend you read that first. A friend of mine says that, to make significant progress in machine learning, one should read research papers on the field. While browsing research papers, I found this one "Beyond triplet loss: a deep quadruplet network for person re-identification" that seemed to be a source of improvement over my previous work and I decided to try to recreate what they have done but for my particular case. This article is about exploring the paper and implementing some of the concepts in the research paper with Keras.

Essential Math for Data Science: Integrals And Area Under The Curve - KDnuggets


Calculus is a branch of mathematics that gives tools to study the rate of change of functions through two main areas: derivatives and integrals. In the context of machine learning and data science, you might use integrals to calculate the area under the curve (for instance, to evaluate the performance of a model with the ROC curve, or to calculate probability from densities. In this article, you'll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models. Building from this example, you'll see the notion of the area under the curve and integrals from a mathematical point of view (from my book Essential Math for Data Science). Let's say that you would like to predict the quality of wines from various of their chemical properties. You want to do a binary classification of the quality (distinguishing very good wines from not very good ones). You'll develop methods allowing you to evaluate your models considering imbalanced data with the area under the Receiver Operating Characteristics (ROC) curve.

Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity


Among the coronaviruses that infect humans, four cause mild common colds, whereas three others, including the currently circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), result in severe infections. Shrock et al. used a technology known as VirScan to probe the antibody repertoires of hundreds of coronavirus disease 2019 (COVID-19) patients and pre–COVID-19 era controls. They identified hundreds of antibody targets, including several antibody epitopes shared by the mild and severe coronaviruses and many specific to SARS-CoV-2. A machine-learning model accurately classified patients infected with SARS-CoV-2 and guided the design of an assay for rapid SARS-CoV-2 antibody detection. The study also looked at how the antibody response and viral exposure history differ in patients with diverging outcomes, which could inform the production of improved vaccine and antibody therapies. Science , this issue p. [eabd4250][1] ### INTRODUCTION A systematic characterization of the humoral response to severe acute respiratory system coronavirus 2 (SARS-CoV-2) epitopes has yet to be performed. This analysis is important for understanding the immunogenicity of the viral proteome and the basis for cross-reactivity with the common-cold coronaviruses. Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is notable for its variable course, with some individuals remaining asymptomatic whereas others experience fever, respiratory distress, or even death. A comprehensive investigation of the antibody response in individuals with severe versus mild COVID-19—as well as an examination of past viral exposure history—is needed. ### RATIONALE An understanding of humoral responses to SARS-CoV-2 is critical for improving diagnostics and vaccines and gaining insight into variable clinical outcomes. To this end, we used VirScan, a high-throughput method to analyze epitopes of antiviral antibodies in human sera. We supplemented the original VirScan library with additional libraries of peptides spanning the proteomes of SARS-CoV-2 and all other human coronaviruses. These libraries enabled us to precisely map epitope locations and investigate cross-reactivity between SARS-CoV-2 and other coronavirus strains. The original VirScan library allowed us to simultaneously investigate antibody responses to prior infections and viral exposure history. ### RESULTS We screened sera from 232 COVID-19 patients and 190 pre–COVID-19 era controls against the original VirScan and supplemental coronavirus libraries, assaying more than 108 antibody repertoire–peptide interactions. We identified epitopes ranging from “private” (recognized by antibodies in only a small number of individuals) to “public” (recognized by antibodies in many individuals) and detected SARS-CoV-2–specific epitopes as well as those that cross-react with common-cold coronaviruses. Several of these cross-reacting antibodies are present in pre–COVID-19 era samples. We developed a machine learning model that predicted SARS-CoV-2 exposure history with 99% sensitivity and 98% specificity from VirScan data. We used the most discriminatory SARS-CoV-2 peptides to produce a Luminex-based serological assay, which performed similarly to gold-standard enzyme-linked immunosorbent assays. We stratified the COVID-19 patient samples by disease severity and found that patients who had required hospitalization exhibited stronger and broader antibody responses to SARS-CoV-2 but weaker overall responses to past infections compared with those who did not need hospitalization. Further, the hospitalized group had higher seroprevalence rates for cytomegalovirus and herpes simplex virus 1. These findings may be influenced by differences in demographic compositions between the two groups, but they raise hypotheses that may be tested in future studies. Using alanine scanning mutagenesis, we precisely mapped 823 distinct epitopes across the entire SARS-CoV-2 proteome, 10 of which are likely targets of neutralizing antibodies. One cross-reactive antibody epitope in S2 has been previously suggested to be neutralizing and, as it exists in pre–COVID-19 era samples, could affect the severity of COVID-19. ### CONCLUSION We present a highly detailed view of the epitope landscape within the SARS-CoV-2 proteome. This knowledge may be used to produce diagnostics with improved specificity and can provide a stepping stone to the isolation and functional dissection of both neutralizing antibodies and antibodies that might exacerbate patient outcomes through antibody-dependent enhancement or immune distraction. Our study reveals notable correlations between COVID-19 severity and both viral exposure history and overall strength of the antibody response to past infections. These findings are likely influenced by demographic covariates, but they generate hypotheses that may be tested with larger patient cohorts matched for age, gender, race, and other demographic variables. ![Figure][2] SARS-CoV-2 epitope mapping. VirScan detects antibodies against SARS-CoV-2 in COVID-19 patients with severe and mild disease. Heatmap color represents the strength of the antibody response in each sample (columns) to each protein (rows, left) or peptide (rows, right). VirScan reveals the precise positions of epitopes, which can be mapped onto the structure of the spike protein (S). Examination of SARS-CoV-2 and seasonal coronavirus sequence conservation explains epitope cross-reactivity. A, Ala; D, Asp; E, Glu; F, Phe; I, Ile; K, Lys; L, Leu; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; Y, Tyr. Understanding humoral responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for improving diagnostics, therapeutics, and vaccines. Deep serological profiling of 232 coronavirus disease 2019 (COVID-19) patients and 190 pre–COVID-19 era controls using VirScan revealed more than 800 epitopes in the SARS-CoV-2 proteome, including 10 epitopes likely recognized by neutralizing antibodies. Preexisting antibodies in controls recognized SARS-CoV-2 ORF1, whereas only COVID-19 patient antibodies primarily recognized spike protein and nucleoprotein. A machine learning model trained on VirScan data predicted SARS-CoV-2 exposure history with 99% sensitivity and 98% specificity; a rapid Luminex-based diagnostic was developed from the most discriminatory SARS-CoV-2 peptides. Individuals with more severe COVID-19 exhibited stronger and broader SARS-CoV-2 responses, weaker antibody responses to prior infections, and higher incidence of cytomegalovirus and herpes simplex virus 1, possibly influenced by demographic covariates. Among hospitalized patients, males produce stronger SARS-CoV-2 antibody responses than females. [1]: /lookup/doi/10.1126/science.abd4250 [2]: pending:yes

Practical Machine Learning Tutorial: Part.4 (Model Evaluation-2)


In this part, we will elaborate on more model evaluation metrics specifically for multi-class classification problems. Learning curves will be discussed as a tool to come up with an idea of how to trade-off between bias and variance in the model parameter selection. ROC curves for all classes in a specific model will be shown to see how false and true positive rate varies through the modeling process. Finally, we will select the best model and examine its performance on blind well data(data that was not involved in any of the processes up to now). This post is the fourth part(final) of part1, part2, part3.

Concordia University coronavirus 'outbreak' attributed to more than 50 'false positives'

Los Angeles Times

Concordia University in Irvine will discontinue its use of antigen testing for asymptomatic students and employees, after more than 50 false positives prompted unwarranted concern about a possible major coronavirus outbreak. As of Wednesday, university officials said there were six active cases -- four students and two employees -- on campus as opposed to the more than 60 infections reported two days ago. Testing in another six cases has not been confirmed, and 55 students and employees have been confirmed as negative for the virus, they said. Campus officials had canceled athletic practices and urged against out-of-state travel for Thanksgiving because of the erroneous test results, which were preliminary pending confirmation from an outside lab. The university previously had been posting only confirmed test results on its COVID-19 dashboard, but made an exception for the unconfirmed numbers because of the indication of a "potential outbreak."

ROC Curve Explained in One Picture


With a ROC curve, you're trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve AuC). The ideal curve in the left image fills in 100%, which means that you're going to be able to distinguish between negative results and positive results 100% of the time (which is almost impossible in real life). The further you go to the right, the worse the detection. The ROC curve to the far right does a worse job than chance, mixing up the negatives and positives (which means you likely have an error in your setup).

The Best Machine Learning Algorithm for Handwritten Digits Recognition


Handwritten Digit Recognition is an interesting machine learning problem in which we have to identify the handwritten digits through various classification algorithms. There are a number of ways and algorithms to recognize handwritten digits, including Deep Learning/CNN, SVM, Gaussian Naive Bayes, KNN, Decision Trees, Random Forests, etc. In this article, we will deploy a variety of machine learning algorithms from the Sklearn's library on our dataset to classify the digits into their categories. The dataset contains a total of 1797 sample points. The DESCR provides a description of the dataset.

Unfolding the Maths behind Ridge and Lasso Regression!


This article was published as a part of the Data Science Blogathon. Many times we have come across this statement – Lasso regression causes sparsity while Ridge regression doesn't! But I'm pretty sure that most of us might not have understood how exactly this works. Let's try to understand this using calculus. First, let's understand what sparsity is.

Positive and Unlabeled Materials Machine Learning


Many real-world problems involve datasets where only some of the data is labeled and the rest is unlabeled. In this post, we discuss our implementation of semi-supervised learning for predicting the synthesizability of theoretical materials. When we think about the materials that will enable next-generation technologies, it's probably not the case that there is one ultimate material waiting to be found that will solve all our problems. The problems we need to solve (producing and storing clean energy, mitigating climate change, desalinating water, etc.) are complex and varied. Even zooming in to the next-generation of electronics, computers, and nanotechnology, there probably isn't a single perfect material to exploit in the same way that silicon has been used in all our familiar devices.