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CNN(Convolutional Neural Networks)

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  Introduction: Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that have revolutionized the field of computer vision. They are specifically designed to process image data and have been widely adopted in applications such as image classification, object detection, and image segmentation. In this blog, we will delve into the workings of a CNN and understand its key components. The Key Components of a CNN A CNN is made up of multiple layers including convolutional layers, activation layers, pooling layers, and fully connected layers. Let's take a closer look at each of these components: Convolutional Layer: The convolutional layer is the heart of a CNN and is responsible for performing the convolution operation on the input image. The convolution operation involves applying a set of filters to the input image to extract important features. These filters can be thought of as small matrices that are multiplied with small regions of the image to produce a new ...
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Ridge, Lasso And ElasticNet Regression .

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  OVERVIEW This blog is going to be very interested for the understanding point of view for ridge ,lasso and elastic net regression . Let first understand what is lasso and Ridge and  ElasticNet. Before moving to Ridge, Lasso and Elastic Net Regression we need an basic idea of Overfitting and Underfitting. Let have an brief idea about it. Overfitting and Underfitting    Overfitting  is a condition where bias is low but variance tends to get high and results in fitting too much in a model, whereas in  Underfitting , the variance is low but bias tends to get high and the model becomes too loose or simplified. Also, when we can’t examine anything from the  machine learning algorithm ,  we can say it is an underfitting condition, whereas, when data provides excessive information that we don’t even need and this data is acting as a burden, we say it is an overfitting problem. Graphical representation of Underfitting, Just Fitting, and Overfitting The p...
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Performance Metrices for classification and Regression.

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  Performance Metrics in Machine Learning Evaluating the performance of a Machine learning model is one of the important steps while building an effective ML model.  To evaluate the performance or quality of the model, different metrics are used, and these metrics are known as performance metrics or evaluation metrics.  These performance metrics help us understand how well our model has performed for the given data. In this way, we can improve the model's performance by tuning the hyper-parameters. Each ML model aims to generalize well on unseen/new data, and performance metrics help determine how well the model generalizes on the new dataset. In machine learning, each task or problem is divided into  classification  and  Regression . Not all metrics can be used for all types of problems; hence, it is important to know and understand which metrics should be used. Different evaluation metrics are used for both Regression and Classification tasks. In this top...
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