πŸ““ Note Repository
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  • β–Ά πŸ“ Courses
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      • πŸ“„ Lecture-1
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    • β–Ά πŸ“ Signal Processing
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        • πŸ“„ Untitled
      • β–Ά πŸ“ Fundamentals
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  • β–Ό πŸ“ Deep Learning
    • πŸ“„ Attention Mechanism
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    • πŸ“„ Data-Driven Inductive Bias
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    • πŸ“„ Deterministic vs Probabilistic Models
    • πŸ“„ Different ML Optimizers
    • πŸ“„ Diffusion Models
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    • πŸ“„ Mixture of Experts
    • πŸ“„ ML Notes Database Expansion
    • πŸ“„ Moravec's Paradox
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    • πŸ“„ Predicting Next Word with GPT-2
    • πŸ“„ Reimannian Manifolds
    • πŸ“„ Solvable Models in Machine Learning
    • πŸ“„ Stability vs Plasticity
    • πŸ“„ The Manifold Hypothesis
    • πŸ“„ VQ-VAE (Vector Quantized Variational Autoencoder)
    • πŸ“„ What is MoCo?
    • πŸ“„ What is Perplexity in ML?
    • πŸ“„ x_0-parameterization
  • β–Ά πŸ“ DSA and CP
    • β–Ά πŸ“ Binary Search
      • πŸ“„ Find 1st and last pos of element in sorted array
      • πŸ“„ Find Peak Element
      • πŸ“„ Koko eating Banana
      • πŸ“„ Note on Binary Search Methods
      • πŸ“„ Search in 2d - II
      • πŸ“„ Search in a 2d array
      • πŸ“„ Search in Rotated Array
      • πŸ“„ Single Element in a Sorted array of pairs
      • πŸ“„ Split Array Largest Sum
    • β–Ά πŸ“ Binary Trees
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      • πŸ“„ Nearest 0 from cell in a matrix
      • πŸ“„ Rotten Oranges
      • πŸ“„ Topological Sort
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      • πŸ“„ Heaps (Priority Queues) β€” Detailed Notes
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      • πŸ“„ Sieve of Eratosthenes
      • πŸ“„ Single Number Others Twice
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      • πŸ“„ Fruits Into Basket
      • πŸ“„ Max Consecutive One III
      • πŸ“„ Maximum Erasure Value
    • β–Ά πŸ“ Sorting
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    • β–Ά πŸ“ Strings
      • πŸ“„ CPP Strings CheatSheet
      • πŸ“„ Largest Odd Number in String
    • πŸ“„ Linked List
  • β–Ά πŸ“ Machine Learning
    • πŸ“„ Bias and Variance Trade-off
    • πŸ“„ Boosting
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  • β–Ά πŸ“ Math
    • β–Ά πŸ“ Linear Algebra
      • πŸ“„ Connection between SVD and EVD
      • πŸ“„ Singular Value Decomposition(SVD)
      • πŸ“„ Spectral Norm
    • β–Ά πŸ“ Probability Theory
      • πŸ“„ Convergences in Probability vs Almost Sure
      • πŸ“„ Deterministic Random Variable
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      • πŸ“„ Law of Iterated Expectations
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      • πŸ“„ Total Expectation Theorem
      • πŸ“„ Unique Properties of Gaussian RVs
      • πŸ“„ Well Known Distributions
      • πŸ“„ Ξ± (Significance Level)
    • πŸ“„ What is Kernel(function)?
  • β–Ά πŸ“ Papers
    • β–Ά πŸ“ World Models Reading List
      • πŸ“„ I-JEPA
      • πŸ“„ What to Read
    • πŸ“„
    • πŸ“„ A Compression Perspective on Simplicity Bias
    • πŸ“„ Beyond Neural Scaling Law - Data Pruning improve power law
    • πŸ“„ Boosting Data-Driven Mirror Descent with Randomization,Equivariance, and Acceleration
    • πŸ“„ Cram Less to Fit More
    • πŸ“„ Crowd-Aware Robot Navigation with Switching BetweenLearning-Based and Rule-Based Methods Using Normalizing Flows
    • πŸ“„ Deep Learning on a Data Diet
    • πŸ“„ Diffusion-LM
    • πŸ“„ Information Theory for Repr Learning
    • πŸ“„ Invariant Risk Minimization
    • πŸ“„ Large-Scale Pruning using Dyna Uncertainty
    • πŸ“„ Learned feature representations are biased by complexity, learning order, position, and more
    • πŸ“„ Learning Stable Normalizing-Flow Control for Robotic Manipulation
    • πŸ“„ Learning to Theorise
    • πŸ“„ Learning2Optimize
    • πŸ“„ MACO-Feature Visualization via Magnitude Constrained Optimization
    • πŸ“„ MaxEnRL via Energy-based Normalising Flows 1
    • πŸ“„ Mechanistic Mode Connectivity
    • πŸ“„ Neural Ordinary Differential Equations
    • πŸ“„ RecoveryChaining-Learning Local Recovery Policies for Robust Manipulation
    • πŸ“„ RESPO (Reachability Estimation for Safe Policy Optimization)
    • πŸ“„ Scaling for Data Filtering
    • πŸ“„ TIC-CLIP - continual training of clip models
    • πŸ“„ Understanding BlackBox Predictions - Using Influence Functions
    • πŸ“„ Understanding Visual Feature Reliance through the Lens of Complexity
    • πŸ“„ When Do Curricula Work?
  • β–Ά πŸ“ Projects
    • β–Ά πŸ“ AlphaGo-Black-Hole
      • πŸ“„ Alpha-Beta Algorithm (Chess Engines)
      • πŸ“„ AlphaZero and Chess
      • πŸ“„ AlphaZero Training
      • πŸ“„ Alphazero Workflow
      • πŸ“„ Improved Representations
      • πŸ“„ Mixture of Experts Approach to Games
    • β–Ά πŸ“ DLM-Revisited
      • πŸ“„ Workbench
  • πŸ“„ Compositional Understanding leads to Causal
  • πŸ“„ Cool Things I Read
  • πŸ“„ DIFFUSION POSTERIOR SAMPLING FOR GENERAL NOISY INVERSE PROBLEMS
  • πŸ“„ Explaining Data-Mixing Scaling Laws
  • πŸ“„ Fun Resources
  • πŸ“„ Hand of Straights
  • πŸ“„ House Robber II
  • πŸ“„ How Feature Learning can Improve Neural Scaling
  • πŸ“„ Longest common Prefix and Sorting a String
  • πŸ“„ Not all solutions are created equal
  • πŸ“„ Professors and Labs
  • πŸ“„ Untitled
  • πŸ“„ Untitled 1
  • πŸ“„ Untitled 2
  • πŸ“„ Untitled 3

Utsab's Note Repository

Reimannian Manifolds

Last modified: July 21, 2026

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