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Deep Learning in Regression: Exploring VGG and LeNet-5 Architectures

Deep learning has revolutionized how we approach complex problems in machine learning, particularly regression analysis. Traditionally, regression models relied heavily on statistical methods and assumptions. However, deep learning offers a powerful alternative: leveraging neural networks with multiple hidden layers to extract patterns and make predictions from data, even in non-linear and high-dimensional spaces.

In this article, we’ll dive into two significant architectures in the deep learning world: VGG and  lenet 5 architecture. Both have played pivotal roles in shaping modern neural network designs, and their concepts can be extended to regression tasks. Let's explore how these architectures operate, their unique characteristics, and their relevance in the broader context of deep learning regression.

Understanding Regression in Deep Learning

Regression tasks involve predicting continuous outputs, such as forecasting temperatures, stock prices, or even modeling relationships between variables. Traditional regression models, like linear regression or…