πŸ““ Note Repository
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      • πŸ“„ Challenge Details
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      • πŸ“„ Lecture-1
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        • πŸ“„ Untitled
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    • πŸ“„ Attention Mechanism
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    • πŸ“„ Curriculum Learning
    • πŸ“„ Data Attribution
    • πŸ“„ Data-Driven Inductive Bias
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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–Ό πŸ“ DSA and CP
    • β–Ό 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
      • πŸ“„ Binary Search Trees
      • πŸ“„ Binary Trees
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      • πŸ“„ Inorder Traversal of a BT
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      • πŸ“„ Maximum Erasure Value
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      • πŸ“„ Merge Sorted Array
      • πŸ“„ Quick Sort
      • πŸ“„ Quick Sort Partition
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      • 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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    • πŸ“„ Training Stabilization in Machine Learning
    • πŸ“„ Word2Vec
  • 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“„ Independent Random Variables
      • πŸ“„ Jointly Gaussian Random Variables β€” Conditional Distributions
      • 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
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    • 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

πŸ“ Binary Search

Section folder view.

Notes

πŸ“„

Find 1st and last pos of element in sorted array

Note
πŸ“„

Find Peak Element

Note
πŸ“„

Koko eating Banana

Note
πŸ“„

Note on Binary Search Methods

Note
πŸ“„

Search in 2d - II

Note
πŸ“„

Search in a 2d array

Note
πŸ“„

Search in Rotated Array

Note
πŸ“„

Single Element in a Sorted array of pairs

Note
πŸ“„

Split Array Largest Sum

Note

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