SVM

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ANN Series - 10 - Backpropagation of Errors

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Watch the video to understand the forward pass in an ANN. Backpropagation, short for "backward propagation of errors," is a fundamental algorithm used for training artificial neural networks. It efficiently computes the gradient of the loss function with respect to the weights of the network, which is essential for adjusting the weights and minimizing the loss through gradient descent or other optimization techniques. The process involves two main phases: a forward pass and a backward pass.     Forward Pass   1. Input Layer:  The input features are fed into the network. 2. Hidden Layers:  Each neuron in these layers computes a weighted sum of its inputs (from the previous layer or the input layer) plus a bias term. This sum is then passed through an activation function to produce the neuron's output. This process repeats layer by layer until the output layer is reached. 3. Output Layer:  The final output of the network is computed, which is then used to cal...
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Naive Bayesian Classifiers - Multinomial, Bernoulli and Gaussian with Solved Examples and Laplace Smoothing

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 Learn about the Naive Bayes Classifier in the following notes. Numeric Example with Dataset (Transactional Data) Sum1 Sum2 Consider the following dataset. Apply the Naïve Bayes classifier to the following frequency table and predict the type of fruit given it is {Yellow, Sweet, Long}. Solution can be viewed in the following pdf. Numeric Example with Text Data Multinomial Naive Bayes vs Bernoulli Naive Bayes Multinomial Naive Bayes and Bernoulli Naive Bayes are both variations of the Naive Bayes algorithm, and they are used for different types of data distributions: 1. Multinomial Naive Bayes:    - The Multinomial Naive Bayes classifier is used for data that is multinomially distributed, which typically means it is used for discrete data.    - It is particularly suitable for text classification problems where features (or words) can occur multiple times. For example, it can be used for document classification where the features are the frequencies...
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Clustering - K means Clustering

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K-means clustering is a popular unsupervised learning algorithm used to partition a dataset into a set of distinct, non-overlapping groups (or clusters) based on similarity. The goal is to organize the data into clusters such that data points within a cluster are more similar to each other than to those in other clusters. The "K" in K-means represents the number of clusters to be identified from the data, and it must be specified a priori. This method is widely used in data mining, pattern recognition, image analysis, and machine learning for its simplicity and efficiency, especially in handling large datasets.   How K-means Clustering Works   K-means clustering follows a straightforward iterative procedure to partition the dataset:   1. Initialization:  Choose K initial centroids randomly or based on a heuristic. Centroids are the center points of the clusters.   2. Assignment Step:  Assign each data point to the nearest centroid. The "nearest" is ...
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