Bindeshwar S. Kushwaha

Text Classification with Bag of Words and Naive Bayes

Data Handling, Machine Learning, Statistics /

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

Fitting of Poisson Distribution

Artificial Intelligence, Machine Learning, Probability and Statistics /

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

Artificial Intelligence, Deep Learning, Machine Learning, Machine Learning /

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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Understanding Neural Networks: Softmax, Cross-Entropy, and Backpropagation

Deep Learning, Machine Learning /

Understanding Neural Networks: Softmax, Cross-Entropy, and Backpropagation Author: Bindeshwar Singh Kushwaha – PostNetwork Academy Neural Network with Softmax + Cross-Entropy Input Layer: The network receives 3 input features, denoted \(x_1, x_2, x_3\). Hidden Layer: 2 neurons in the hidden layer with activations \(a^{(1)}\) and \(a^{(2)}\). Output Layer: 2 outputs \(z^{(3)}, z^{(4)}\), passed through softmax. Softmax

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Iris Classification Neural Network with Backpropagation

Artificial Intelligence, Machine Learning /

      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 in Python

Artificial Intelligence, Machine Learning /

  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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Understanding Speech Data using Python

Natural Language Processing /

Understanding Speech Data using Python Author: Bindeshwar Singh Kushwaha – Postnetwork Academy Introduction Speech is a continuous acoustic signal that we digitize so computers can analyze and learn from it. This post walks you through what speech data is, how to load and visualize it in Python using librosa, and why both time-domain and frequency-domain

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Introduction to Natural Language Processing

Natural Language Processing /

Introduction to Natural Language Processing Author: Bindeshwar Singh Kushwaha – Postnetwork Academy Introduction Natural Language Processing (NLP) is the field of AI that enables computers to understand, interpret, and generate human language. In this post, you’ll learn what NLP is, its brief history, how it relates to AI/ML/DL, practical applications (from translation to chatbots), and

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Reading, Saving, and Displaying an Image in Python

Artificial Intelligence, Machine Learning, Natural Language Processing /

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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