What are the different NLP tasks deep learning can be applied?
1. Machine translation, 2. Sentiment Analysis, 3. Question and Answer system1. Machine translation : Sequence to sequence models are used for this.2. Sentiment Analysis : Classification techniques on text using neural networks3. Question and Answer system : This is again a Seq to seq model
What are some key business metrics for (S-a-a-S startup | Retail bank | e-Commerce site)?
Why is Area Under ROC Curve (AUROC) better than raw accuracy as an out-of- sample evaluation metric?
What is the ROC Curve and what is AUC (also called as AUROC)?
Data Preprocessing Techniques
Three data preprocessing techniques to handle outliers are:Winsorize (cap at threshold).Transform to reduce skew (using Box-Cox or similar).Remove outliers if you're certain they are anomalies or measurement errors.
In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. An outlier can cause serious problems in statistical analyses.
What is the Box-Cox transformation used for? Can you give your opinion?
Which one is best either RELU or SIGMOID
RELU Sigmoid or some of the activtivation function commonly used but my doubt is how I know when to use RELU and when to use SIGMOID?
Finding accuracy of algorithm
For a particular case there are different types of algorithm, How to find a better algorithm for a problem before testing the accuracy?
In theoretical computer science, correctness of an algorithm is asserted when it is said that the algorithm is correct with respect to a specification. Functional correctness refers to the input-output behavior of the algorithm (i.e., for each input it produces the expected output).
My recently published article on Hybrid Machine Learning
Hello friends,I recently wrote an article on hybrid machine learning. It is titled "Hybrid Machine Learning Explained in Nontechnical Terms". The objective was to present a very simple explanation of the hybrid approach to machine learning and solutions development to machine learning and data science enthusiasts out there. I hope someone here will find use exciting to read too.The link is here:https://pubs.spe.org/en/dsde/dsde-article-detail-page/?art=6583I will appreciate your critical feedback.Educating the young professionals within the machine learning community is one of my main objectives.Enjoy the article ...
Good article. Detailed describtion about The conventional ML work flow and Hybrid Machine Learning work flow.
Its not magical. Black box algorithms are the complex code at the heart of systems
In science, computing, and engineering, a black box is a device, system or object which can be viewed in terms of its inputs and outputs, without any knowledge of its internal workings. Its implementation is "opaque".