Chawla

Actress-environmentalist Juhi Chawla Monday approached the Delhi High Court against the setting up of 5G wireless networks in the country, raising issues related to the radiation impact on citizens, animals, flora and fauna.Justice C Hari Shankar, before whom the matter came up for hearing, transferred the suit to another bench for hearing on June 2.Chawla said if the telecommunications industry's plans for 5G come to fruition, no person, no animal, no bird, no insect, and no plant on Earth will be able to avoid exposure,…

The Delhi High Court on Wednesday questioned actress-environmentalist Juhi Chawla for directly filing a suit against setting up of 5G wireless networks in the country without giving any representation to the government on her concerns related to the technology.Justice J R Midha said the plaintiffs, Chawla and two others, were required to first approach the government for their rights and if denied, they should come to the court.The court after hearing the arguments of various parties reserved its order on the suit.The…

Things you can do in one line using PandasPhoto by KirstenMarie on UnsplashTraining data-driven machine learning models has never been as easy as today. For instance, assume you are training a vanilla neural network. Here, adjusting the architecture for the number of hidden layers and their dimension, tweaking the hyperparameters, or changing the loss function can all be done with a slight modification in the model definition or its optimizer.While on one hand, this is advantageous as it reduces the heavy lifting of…

SQL queries and their corresponding methods in PandasPhoto by engin akyurt on UnsplashBoth Pandas and Structured Query Language (SQL) are undoubtedly the go-to tools for Data Scientists for tabular data management, processing, and analysis. While Pandas is a popular Python library for data analysis, SQL is an entire programming language of its own to interact with Databases. One thing that stands out in common between them is that both are excellent tools for working on Tabular Data.Since both Pandas and SQL are…

A set of challenging Pandas QuestionsPhoto by ALAN DE LA CRUZ on UnsplashPandas library has always intrigued Data Scientists to do amazing things with it. It is undoubtedly the go-to tool for tabular data handling, manipulation, and processing.Therefore, to scale your expertise, challenge your existing knowledge, and introduce you to numerous popular Pandas functions among Data Scientists, I am presenting Part 2 of the Pandas Exercise. You can find the Part 1 of the Pandas Exercise here:The objective is to strengthen your…

A guide to p-norms.Photo by Markus Winkler on UnsplashIf you are reading this post, it is likely that you already know what vectors are and their indispensable place in Machine Learning. To recap, Vectors are a one-dimensional array of numbers of a particular length. This is shown below:A vector containing n elements. (Image by author)Elements of a vector are arranged in a particular order, and the location of an element usually holds an inherent meaning. We can access the individual elements using their position (or…

A set of challenging Pandas QuestionsPhoto by Olav Ahrens Røtne on UnsplashPandas library has always intrigued Data Scientists to do amazing things with it. It is undoubtedly the go-to tool for tabular data handling, manipulation, and processing.Therefore, to scale your expertise, challenge your existing knowledge, and introduce you to numerous popular Pandas functions among Data Scientists, I am presenting Part 1 of the Pandas Exercise. The objective is to strengthen your logical muscle and to help internalize data…

Say no to Pandas’ Read CSV method!Photo by Solaiman Hossen on UnsplashOne major caveat of Pandas is its inability to expand to multiple CPU cores (usually). Essentially, when you run any operation, where ideally parallelization is possible, Pandas would still stick to the traditional single-core execution and the other cores would sit idle. This gets especially concerning when you are building large-scale applications.Working with large-scale data-driven applications demands faster execution through optimized algorithms…

Discussing another major caveat of PandasWhite and Black Love Print Textile (source: Pexels)Input-output operations with Pandas to a CSV are serialized, making them incredibly inefficient and time-consuming. It's frustrating when I see ample scope for parallelization here, but unfortunately, Pandas does not provide this functionality (yet). Although I am never in favor of creating CSVs in the first place with Pandas (read my post below to know why), I understand that there might be situations where one has no other choice…