| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Seeing the Structure PCA misses | |||
| 1 - Uncovering Non-Linear Manifolds When Linear Methods Fail.mp4 | 168.3 MB | ||
| 2 - Applying t-SNE and reading it correctly | |||
| 3 - Applying UMAP for visualization and beyond | |||
| 4 - Scaling Embeddings with UMAP Global Structure & Feature Projection.mp4 | 301.5 MB | ||
| 4 - Visualizing responsibly and debugging real embeddings | |||
| 5 - Embedding Diagnostics Trustworthiness Metrics & Distortion Debugging.mp4 | 278.7 MB | ||
| 5 - Kernel Principal Component Analysis and Pre-Image Reconstruction Strategies | |||
| 6 - REAL-WORLD ASSETS (Downloadable) | |||
| 01_embedding_pipeline_template.pdf | 71.5 KB | ||
| 02_embedding_audit_checklist.pdf | 44.3 KB | ||
| 03_faq_nonlinear_reduction.pdf | 36.4 KB | ||
| 04_swipe_file_real_examples.pdf | 42.5 KB | ||
| 05_action_plan_worksheet.pdf | 46.1 KB | ||
| 06_hands_on_lab_workbook.pdf | 55.1 KB | ||
| 10 - 01_Core_Pipeline_Script.pdf | 4.9 KB | ||
| 10 - 02_Cheat_Sheet_and_Checklists.pdf | 6.2 KB | ||
| 10 - 03_FAQ.pdf | 4.3 KB | ||
| 10 - 04_Swipe_File_5_Real_Examples.pdf | 4.1 KB | ||
| 10 - 05_Action_Plan_Worksheet.pdf | 3.5 KB | ||
| 10 - 06_Final_Project_Brief_and_Rubric.pdf | 3.4 KB | ||
| 10 - REAL-WORLD ASSETS (Downloadable).html | 18 KB | ||
| Nonlinear Dimensionality Reduction and Visualization.md | 9.7 KB | ||
| SKILL.md | 3.7 KB | ||
| 6 - Robust Topology.mp4 | 107.1 MB | ||
| 7 - Strengthening_Kernel_PCA_Backward_Mapping.mp4 | 53.1 MB | ||
| 8 - Topological Data Analysis.mp4 | 80.1 MB | ||
| 9 - Untangling_Reality_with_kPCA.mp4 | 61.5 MB | ||
| 3 - Mastering t-SNE Perplexity Tuning and Misinterpretation Pitfalls.mp4 | 276.4 MB | ||
| 2 - Uncovering Non-Linear Manifolds When Linear Methods Fail (PART2).mp4 | 140.3 MB |
Nonlinear Dimensionality Reduction: t-SNE, UMAP & Python
https://WebToolTip.com
Last updated 10/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 16m | Size: 1.43 GB
Master nonlinear dimensionality reduction with t-SNE, UMAP and Kernel PCA to visualize high-dimensional data via Python.
What you'll learn
Identify non-linear manifold structures where standard linear PCA fails and select optimal non-linear dimensionality reduction algorithms for high-D data.
Implement t-SNE and UMAP, tune hyperparameters like perplexity and n_neighbors, and eliminate common visual cluster misinterpretations.
Quantify embedding distortion using trustworthiness and continuity metrics to debug and validate complex tabular and text embeddings.
Build Kernel PCA pre-image reconstructions and interactive TDA Mapper topological graph networks in Python for real-world production pipelines.
Requirements
Intermediate Python skills (NumPy, Pandas, Matplotlib) and basic familiarity with machine learning and linear algebra concepts.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 3 GB | freecoursewb | 1 year | 0 | 1 | |
| 3.3 GB | freecoursewb | 1 year | 0 | 1 |
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