syedanazish-debug/lab-ai-ml

How syedanazish-debug/lab-ai-ml works

This repository is a set of academic Python practicals, not one integrated application. The main workflow is a learner selecting and running a subject practical to observe printed results or visualizations. The graph preserves the documented NLP, neural-network, and image-processing topics; source excerpts are available for only some programs, so links between individual practicals are omitted where execution or data flow is not evidenced.

Main entry points: DIP Run DIP practical · NN_PRACTICLE Run NN practical · NLP_PRACTICLE Run NLP practical · 4th_par.py Sentence segmentation

Components 25

NLP practicals

Run NLP practical NLP_PRACTICLE
Sentence segmentation NLP_PRACTICLE/4th_par.py
Brown corpus analysis NLP_PRACTICLE/2nd_par.py
Treebank tagging NLP_PRACTICLE/3rd_par.py
Text results

Neural networks

Run NN practical NN_PRACTICLE
Neuron models NN_PRACTICLE/p1_mp_neuron.py
Perceptron learning NN_PRACTICLE/p4_sl_perceptron.py
Winner-take-all NN_PRACTICLE/p7_winner_comp.py
Convergence practical NN_PRACTICLE/p8_convergence_th.py
Backpropagation NN_PRACTICLE/p9_bp_ml_peerceptron.py
Adaptive filtering NN_PRACTICLE/P10_.py
Image convolution NN_PRACTICLE/p12_.py
Predictions and plots

Image processing

Run DIP practical DIP
Color components DIP/p1_color_m.py
Image logic operations DIP/p2_logic_opt.py
Gray-level processing DIP/p3_graylevel.py
Histogram processing DIP/p4_hist.py
Discrete Fourier transform DIP/p5_DFT.py
Video convolution DIP/p6_v_com.py
Run-length encoding DIP/p7_RLE.py
Noise practical DIP/p8_GN.py
Image results

Other components

Learner

Connections 30

Learner → Run NLP practical: selects and runs
Run NLP practical → Sentence segmentation: runs
Run NLP practical → Brown corpus analysis: runs
Run NLP practical → Treebank tagging: runs
Sentence segmentation → Text results: prints segments
Brown corpus analysis → Text results: prints statistics
Treebank tagging → Text results: prints corpus data
Learner → Run NN practical: selects and runs
Run NN practical → Neuron models: runs
Run NN practical → Perceptron learning: runs
Run NN practical → Winner-take-all: runs
Run NN practical → Convergence practical: runs
Run NN practical → Backpropagation: runs
Run NN practical → Adaptive filtering: runs
Run NN practical → Image convolution: runs
Backpropagation → Predictions and plots: prints predictions
Adaptive filtering → Predictions and plots: plots filter output
Image convolution → Predictions and plots: displays filtered images
Learner → Run DIP practical: selects and runs
Run DIP practical → Color components: runs
Run DIP practical → Image logic operations: runs
Run DIP practical → Gray-level processing: runs
Run DIP practical → Histogram processing: runs
Run DIP practical → Discrete Fourier transform: runs
Run DIP practical → Video convolution: runs
Run DIP practical → Run-length encoding: runs
Run DIP practical → Noise practical: runs
Color components → Image results: displays components
Image logic operations → Image results: displays operations
Histogram processing → Image results: plots histograms

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Drawn by GitDiagram on September 29, 2026 from the repository's file tree, README and source files. Read as Markdown ·