Gradient Descent Gradient Descent is the most popular optimizer to update parameters and it uses the gradient of the error with respect to the parameter. But the parameter update rule is different and thus there are different variants of Gradient Descent. Mini- Batch Gradient Decent It is the simplest algorithm, where we update parameters in […]
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How to do Data Science Project
Data science project can be a challenging but rewarding process. By following the steps we can use in this blog, you can work through the project in an organized and effective way, and ultimately arrive at a solution to your problem. Previously, we wrote blogs on many machine learning algorithms (Classification, Predication) as well as […]
Mathematics Behind Support Vector Machine
Support Vector Machine is supervised machine learning algorithm. In this blog, we are going to discuss how mathematically support vector machine works. We will also discuss the types of SVM and how to implement it in Python. So, let’s get started. In the past, we have authored blog posts covering a wide range of topics, […]
Different method of Outliers Detection
Outlier is a data point that is significantly different from the other data points in a sample. Outliers can occur for a variety of reasons, such as errors in measurement or recording, or they can be the result of natural variation in the data. Outliers can have a significant effect on the statistical properties of […]
Naive Bayes From Scratch
Naive Bayes is a machine learning algorithm that is used for classification tasks. It is called "naive" because it makes a simplifying assumption about the data, specifically that the features in the data are independent of one another. Despite this assumption, the algorithm has been shown to be effective in many real-world applications. Previously, we […]
Different Method of Model Evaluation(Part2)
Model evaluation is the process of assessing the performance of a machine learning model on a data set. It is an important step in the development of a machine learning model, as it allows us to determine how well the model is able to make accurate predictions on unseen data. Previously, we wrote blogs on […]
Different Method of Model Evaluation(Part-1)
Accuracy is a metric that is used to evaluate the performance of a model on a classification task. It is the fraction of correct predictions made by the model on a data set, expressed as a percentage. For example, if a model makes 90 correct predictions out of 100, its accuracy is 90%. Previously, we […]
How Mathematically Naive Bayes Classifier Works?
Previously, we wrote blogs on many machine learning algorithms as well as many other topics to help you broaden your knowledge. Please kindly visit our site and we would be glad if we got some feedback from you to improve our writing. To see some of them, you can follow the mentioned links. Linear Regression […]
Stock Price Prediction On Commercial Data Using GRU
GRU are a kind of Neural Networks which are designed to overcome some issues with RNN. There are two primary issues with recurrent neural networks Calculations of gradients either fail or explode. Gradient calculations are expensive Gradient clipping is a solution to the expanding gradient problem, and other topologies like the gated recurrent unit (GRU) […]
Stock Price Prediction On Commercial Data Using LSTM
LSTMS are a variety of RNNs but lets start with RNNs. RNNs are unable to remember long-term dependencies in time series data because of the vanishing gradient issue. An RNN version called LSTM was created to deal with this problem. Similar to RNN, LSTM features a hidden state that functions as short-term memory. Additionally, it […]