None Archive

Protobuf Vs. Messagepack: What’s The Difference?

When considering data serialization formats, two popular choices are Protocol Buffers (protobuf) and MessagePack. In this article, we’ll analyze protobuf vs messagepack in terms of structure, performance, usage scenarios, and security considerations to help you decide which format is most suitable for your project requirements. So let’s dive into the details of these two

Avro Vs CSV: Which Data Serialization Format Is Right For You?

Avro vs. CSV – two data serialization formats used to store and transmit data. But which one should you use? This blog post takes a look at the distinctions between Avro and CSV with respect to architecture, execution, and applications and gives advice for when each format is most suitable. Table of Contents: Structure

Memory Access Time and Memory Cycle Time

Memory access time and cycle time are two timing methods used to measure the performance of a system. Memory access time measures how long it takes for data to be read from or written to memory, while memory cycle time measures the amount of time it takes for a computer’s memory to complete one

Understanding Memory Cells in Computers

Memory cells are the basic storage units in computer memory. They use capacitors holding electrical charges to store data as binary values. Various types of cells make up this technology, such as SLC (Single-Level Cell), MLC (Multi-Level Cell), eMLC (Enterprise Multi-Level Cell), TLC (Triple-Level Cell), and QLC (Quadruple Level Cells). In this blog post,

Deep Learning Architectures for Object Detection: Yolo vs. SSD vs. RCNN

In this post, we will look at the major deep learning architectures that are used in object detection. We first develop an understanding of the region proposal algorithms that were central to the initial object detection architectures. Then we dive into the architectures of various forms of RCNN, YOLO, and SSD and understand what

Feature Scaling and Data Normalization for Deep Learning

Before training a neural network, there are several things we should do to prepare our data for learning. Normalizing the data by performing some kind of feature scaling is a step that can dramatically boost the performance of your neural network. In this post, we look at the most common methods for normalizing data

Understanding Hinge Loss and the SVM Cost Function

In this post, we develop an understanding of the hinge loss and how it is used in the cost function of support vector machines. Hinge Loss The hinge loss is a specific type of cost function that incorporates a margin or distance from the classification boundary into the cost calculation. Even if new observations

The Softmax Function and Multinomial Logistic Regression

In this post, we will introduce the softmax function and discuss how it can help us in a logistic regression analysis setting with more than two classes. This is known as multinomial logistic regression and should not be confused with multiple logistic regression which describes a scenario with multiple predictors. What is the Softmax

The Sigmoid Function and Binary Logistic Regression

In this post, we introduce the sigmoid function and understand how it helps us to perform binary logistic regression. We will further discuss the gradient descent for the logistic regression model (logit model). In linear regression, we are constructing a regression line of the form y = kx + d. Within the specified range,

Type 1 and Type 2 Error

When you are testing hypotheses, you might encounter type 1 and type 2 errors. Identifying them and dealing with them is essential for setting up statistical testing scenarios. They also play a huge role in machine learning. What is a Type 1 Error in Statistics? When you reject the null hypothesis although it is