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Weight Initialization?When training a neural network, one of the things we should care about is where we should start on the loss function to reach the optimal solution more easily and quickly.When training a neural network, the method of determining the starting position on the loss function is called Model Initialization.In particular, weights account for the largest portion of the model's par..

In Neural Networks, the activation function is a key element that enables the network to learn complex patterns and non-linearity.Reasons why activation functions are necessary:Introduction of Non-linearityWithout activation functions, neural networks would be merely combinations of linear transformations.No matter how many layers are stacked, they could be expressed as a single linear function,..

What is Drop-out?Drop-out is one of the methods to reduce overfitting in neural networks.Drop-out is not the only method to avoid overfitting.There are various methods including regularization, and while weight reduction methods like regularization are simple to implement and can avoid overfitting to some extent, when neural network models become complex, it becomes difficult to cope with overfi..

Birth of Logistic Loss FunctionIn linear regression, the goal was to find weights and bias that minimize the squared error between the actual and predicted values. Besides, the goal of classfication problems like logistic regression is to increase the actual ratio of correctly classified samples data itself. However, the ratio of correctly classified samples is not a differentiable function. In ..

Polars?Pandas library is widely used for data analysis and processing because it is flexible and convenient to use.However, Pandas has limitations in handling large-scale data due to processing speed and memory usage constraints when dealing with big data.To address this issues, Polars has emerged as a new data processing tool. Polars is implemented in Rust that offers great performance for proc..

Briefly About TabNetTabNet is a deep learning architecture developed by Google in 2019 for analyzing tabular data.In many competitions, including Kaggle, tree-based ensemble models like XGBoost and LightGBM usually dominate the leaderboards when it comes to tabular data analysis.While deep learning leads in analyzing unstructured data like images, text, and audio, tree-based methods have been wi..