Principal Component Analysis (PCA), Step-by-Step

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Best Practices for Storing and Loading JSON Objects from a Large SQL Server Table Using .NET Core

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 Best Practices for Storing and Loading JSON Objects from a Large SQL Server Table Using .NET Core Introduction JSON (JavaScript Object Notation) is widely used for storing structured data in SQL Server. However, when dealing with large tables, inefficient handling of JSON data can lead to performance bottlenecks, high storage costs, and slow queries. In this article, we will cover best practices for saving and loading JSON objects from large SQL Server tables using .NET Core. We will focus on storage strategies, indexing, efficient querying, and optimized data retrieval using Entity Framework Core (EF Core) and Dapper. --- 1. Choosing the Right Storage Strategy SQL Server supports JSON natively, but choosing the correct storage method depends on your use case. Option 1: Store JSON as NVARCHAR(MAX) (Best for Flexibility & Simplicity) ✔ Ideal for semi-structured or frequently changing data. ✔ Suitable when you need to store the entire JSON object but rarely query individual fiel...
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Maxpooling vs minpooling vs average pooling

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Pooling is performed in neural networks to reduce variance and computation complexity. Many times, beginners blindly use a pooling method without knowing the reason for using it. Here is a comparison of three basic pooling methods that are widely used. The three types of pooling operations are: Max pooling: The maximum pixel value of the batch is selected. Min pooling: The minimum pixel value of the batch is selected. Average pooling: The average value of all the pixels in the batch is selected. The batch here means a group of pixels of size equal to the filter size which is decided based on the size of the image. In the following example, a filter of 9x9 is chosen. The output of the pooling method varies with the varying value of the filter size. The operations are illustrated through the following figures. Average, Max and Min pooling of size 9x9 applied on an image We cannot say that a particular pooling method is better over others generally. The choice of pooling operation is made...
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Understand the Softmax Function in Minutes

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Learning machine learning? Specifically trying out neural networks for deep learning? You likely have run into the  Softmax function, a wonderful  activation function  that turns numbers aka logits into probabilities that sum to one. Softmax function outputs a vector that represents the probability distributions of a list of potential outcomes.  It’s also a core element used in  deep learning   classification  tasks. We will help you understand the Softmax function in a beginner friendly manner by showing you exactly how it works — by coding your very own Softmax function in python. If you are implementing Softmax in Pytorch and you already know Pytorch well, scroll down to the Deep Dive section and grab the code. This article has gotten really popular: 5800+ claps. It is updated constantly. Latest update Jan 2020 added a TL;DR section for busy souls. Dec 2019 (Softmax with Numpy Scipy Pytorch functional. Visuals indicating the location of Softmax func...
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