Videos

Visit YouTube Channel

My YouTube Channel

YouTube Logo

Click the logo above to visit my YouTube channel!

Comments

Post a Comment

Popular posts from this blog

About me

Image
  Associate Professor Anil Neerukonda Institute of Technology & Sciences Sangivalsa, Visakhapatnam, A.P., INDIA. VIDWAN | ORCID | SCOPUS | RESEARCH ID | GOOGLE ID
Read more

Principal Component Analysis

  Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional form while preserving as much of the original data’s variance as possible. PCA achieves this by creating new, uncorrelated variables called principal components , which are linear combinations of the original variables. These principal components capture the directions of maximum variance in the data, with the first few components typically containing most of the information. How PCA Works Standardize the Data : Center and scale the data so that each feature has a mean of zero and a variance of one. This step ensures that features with larger scales don’t dominate the results. Compute the Covariance Matrix : Calculate the covariance matrix to understand how features vary with respect to each other. Calculate Eigenvalues and Eigenvectors : Determine the eigenvalues and eigenvectors of the covariance matrix. Eigenvectors define the directions (...
Read more

2 Node Neural Network

  Simple Explanation of a 2-Node Neural Network Architecture Imagine a tiny brain with only 2 brain cells (neurons) that receive information and produce 2 outputs. Input Features [ x₁ ] Weights for Biases [ x₂ ] ──► Neuron 1 ─────► [ b₁ ]───┐ [ x₃ ] W₁₁, W₁₂, … │ [ x₄ ]           ├──► [ Output₁ ] ← Node 1 output (after activation) . │ . │ [ xₙ ] Weights for │ Neuron 2 ─────► [ b₂ ] ─ ─┘ W₂₁, W₂₂, … └──► [ Output₂ ] ← Node 2 output (after activation) Real Example We Used (4 inputs → 2 nodes) Input (4 features) 2-Node Layer (Output) x₁ = 1.0 weight matrix W (4×2) bias Linear Activation Final Output x₂ = 0.5 ───────────────────────► + [b₁] ─────► Z = X·W + b ─────► ReLU(Z) ───► [ y...
Read more