Discrete Uniform Distribution By: Bindeshwar Singh Kushwaha PostNetwork Academy Discrete Uniform Distribution A random variable \( X \) is said to have a discrete uniform distribution if it takes integer values from \( a \) to \( b \) with equal probability. The number of possible values is \[ n = b – a + […]
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Naive Bayes Classification Algorithm for Weather Dataset Author: Bindeshwar Singh Kushwaha | PostNetwork Academy Introduction to Naive Bayes Classifier Naive Bayes is a probabilistic classification algorithm. It is based on Bayes’ Theorem and the naive independence assumption. Suppose we have a feature vector \(\mathbf{X} = (x_1, x_2, …, x_n)\) and a class \(y\). Bayes Theorem:
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Text Classification with Bag of Words and Naive Bayes Author: Bindeshwar Singh Kushwaha | PostNetwork Academy Understanding Text with Machine Learning Processing and understanding text allows extraction of meaningful information from raw data. Text data can be structured into features that machine learning algorithms can analyze. Machine learning approaches include supervised, unsupervised, and deep learning
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Fitting of Poisson Distribution Bindeshwar Singh Kushwaha — PostNetwork Academy Introduction Master the technique of fitting the Poisson distribution to real-world frequency data. This tutorial shows a step-by-step method to calculate theoretical frequencies for observed datasets. Key Concepts & Techniques Introduction to Fitting: Fit a theoretical Poisson distribution to experimental data to derive expected frequencies.
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Gradient of Softmax + Cross-Entropy w.r.t Logits Author: Bindeshwar Singh Kushwaha – PostNetwork Academy Goal We want to compute: $$ \frac{\partial L}{\partial z_j} $$ Notation: Logits: \(z = [z_1, z_2, \dots, z_C]\) Softmax: \(\hat{y}_i = \frac{e^{z_i}}{\sum_{k=1}^{C} e^{z_k}}\) Cross-Entropy Loss: \(L = -\sum_{i=1}^{C} y_i \log \hat{y}_i\), where \(y_i\) is one-hot. [Insert Neural Network Diagram Here] Loss
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Iris Classification Neural Network with Backpropagation Bindeshwar Singh Kushwaha PostNetwork Academy Forward Propagation Step 1 Dataset features: \( x_1 = \text{Sepal length}, \; x_2 = \text{Sepal width}, \; x_3 = \text{Petal length}, \; x_4 = \text{Petal width} \) Forward Propagation Step 2 \( z_{h1} = w_{11}x_1 + w_{21}x_2 + w_{31}x_3 + w_{41}x_4
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Spectrogram of Speech Author: Bindeshwar Singh Kushwaha — PostNetwork Academy What is a Spectrogram? Spectrogram — a visual representation of sound. Shows how the frequency content of a signal changes over time. Axes of a spectrogram: X-axis: Time (seconds) Y-axis: Frequency (Hz) Color / Intensity: Amplitude or Power (dB) Computed using the Short-Time Fourier
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Reading, Saving, and Displaying an Image in Python Author: Bindeshwar Singh Kushwaha – Postnetwork Academy Introduction In Python, working with images is a common requirement for data science, computer vision, and AI projects. Images can be loaded, manipulated, and displayed using various libraries such as matplotlib, scikit-image, and OpenCV. This tutorial demonstrates how to read,
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What is an Image and How it is Stored on a Computer Author: Bindeshwar Singh Kushwaha Published by: Postnetwork Academy 📸 Concept of an Image An image can be thought of as a 2D function: $$ f(x, y) $$ Each point \( (x, y) \) in the function maps to an intensity or color value.
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📘 Poisson Distribution Numerical Examples Author: Bindeshwar Singh Kushwaha Institution: PostNetwork Academy Example 1: Truck Arrivals The number of heavy trucks arriving at a railway station follows a Poisson distribution with an average of 2 arrivals per hour. Find: (a) Probability that no truck arrives (b) Probability that at least two trucks arrive Let \(
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