πŸ““ Note Repository
  • β–Ά πŸ“ Competitions
    • β–Ά πŸ“ ARC-WhiteBox-Estimation Challenge
      • πŸ“„ Challenge Details
    • β–Ά πŸ“ TrexQuant Alpha Challenge
      • πŸ“„ Alpha Info
    • πŸ“„ LPCV-Track-1
    • πŸ“„ Orbit Wars
  • β–Ά πŸ“ Courses
    • β–Ά πŸ“ FM and Diff - MIT CSAIL
      • πŸ“„ Lecture-1
      • πŸ“„ Lecture-2
      • πŸ“„ Overview
    • β–Ά πŸ“ Signal Processing
      • β–Ά πŸ“ Digital Communication
        • πŸ“„ Untitled
      • β–Ά πŸ“ Fundamentals
        • πŸ“„ Fourier Transform
  • β–Ά πŸ“ Deep Learning
    • πŸ“„ Attention Mechanism
    • πŸ“„ Continual Learning
    • πŸ“„ Continual Learning - Eval Metrics
    • πŸ“„ Continual Learning Formulation
    • πŸ“„ Curriculum Learning
    • πŸ“„ Data Attribution
    • πŸ“„ Data-Driven Inductive Bias
    • πŸ“„ Depth Estimation Metrics
    • πŸ“„ Deterministic vs Probabilistic Models
    • πŸ“„ Different ML Optimizers
    • πŸ“„ Diffusion Models
    • πŸ“„ Diffusion Reverse Process
    • πŸ“„ Diffusion-LM Denoising
    • πŸ“„ Energy based Models
    • πŸ“„ FrΓ©chet Inception Distance (FID)
    • πŸ“„ Low Rank Adaptation(LoRA)
    • πŸ“„ Matryoshka Representation Learning
    • πŸ“„ Mixture of Experts
    • πŸ“„ ML Notes Database Expansion
    • πŸ“„ Moravec's Paradox
    • πŸ“„ N-gram Models
    • πŸ“„ Neural Implicit Fields
    • πŸ“„ Optimal Transport
    • 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
      • πŸ“„ Binary Search Trees
      • πŸ“„ Binary Trees
      • πŸ“„ Check for a Balanced BT
      • πŸ“„ Inorder Traversal of a BT
      • πŸ“„ Postorder Traversal of BT
      • πŸ“„ Preorder Inorder Postorder Traversals in One Traversal
    • β–Ά πŸ“ Doubts
      • πŸ“„ Binary Search β€” `<=` vs `<` Boundary Conditions
    • β–Ά πŸ“ Dynamic Programming
      • πŸ“„ Coin Change
      • πŸ“„ Subset sum equal to target
      • πŸ“„ Target Sum
      • πŸ“„ Unbounded Knapsack
    • β–Ά πŸ“ Graphs
      • πŸ“„ Connected Components in a Graph
      • πŸ“„ Course Schedule
      • πŸ“„ Course Schedule II
      • πŸ“„ Detection of a Cycle in a Directed Graph
      • πŸ“„ Detection of Cycle in an Undirected Graph
      • πŸ“„ Nearest 0 from cell in a matrix
      • πŸ“„ Rotten Oranges
      • πŸ“„ Topological Sort
    • β–Ά πŸ“ Greedy
      • πŸ“„ Insert Interval
      • πŸ“„ Jump-II
      • πŸ“„ Task Scheduler
    • β–Ά πŸ“ Heaps
      • πŸ“„ Heaps (Priority Queues) β€” Detailed Notes
    • β–Ά πŸ“ Maths
      • πŸ“„ Sieve of Eratosthenes
      • πŸ“„ Single Number Others Twice
    • β–Ά πŸ“ Sliding WIndow and Two Pointers
      • πŸ“„ Defuse the Bomb
      • πŸ“„ Fruits Into Basket
      • πŸ“„ Max Consecutive One III
      • πŸ“„ Maximum Erasure Value
    • β–Ά πŸ“ Sorting
      • πŸ“„ Merge Sorted Array
      • πŸ“„ Quick Sort
      • πŸ“„ Quick Sort Partition
    • 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
    • πŸ“„ Byte Pair Encoding (BPE) Tokenization
    • πŸ“„ Decision Trees (A comprehensive Note)
    • πŸ“„ K-Means Clustering
    • πŸ“„ Long Short-Term Memory (LSTM)
    • πŸ“„ Principal Component Analysis (PCA)
    • πŸ“„ Random Forests
    • πŸ“„ Recurrent Neural Netoworks(RNNs)
    • πŸ“„ Representational Similarity Analysis (RSA)
    • πŸ“„ t-SNE (t-Distributed Stochastic Neighbor Embedding)
    • 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
      • πŸ“„ Gaussian Distributions
      • πŸ“„ Independent Random Variables
      • πŸ“„ Jointly Gaussian Random Variables β€” Conditional Distributions
      • πŸ“„ Law of Iterated Expectations
      • πŸ“„ Markov, Chebyshev, and Jensen's Inequalities
      • πŸ“„ p-values
      • πŸ“„ Power of Test Statistic
      • πŸ“„ Probability and Statistics
      • πŸ“„ Study Materials
      • 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

Garden Index

Explore the notes, categories, and technical logs from my Obsidian vault.

πŸ“‚ Top-Level Folders

πŸ“

Competitions

4 notes
πŸ“

Courses

5 notes
πŸ“

Deep Learning

32 notes
πŸ“

DSA and CP

44 notes
πŸ“

Machine Learning

13 notes
πŸ“

Math

19 notes
πŸ“

Papers

28 notes
πŸ“

Projects

7 notes

πŸ“„ Root Notes

πŸ“„

Compositional Understanding leads to Causal

Note
πŸ“„

Cool Things I Read

Note
πŸ“„

DIFFUSION POSTERIOR SAMPLING FOR GENERAL NOISY INVERSE PROBLEMS

Note
πŸ“„

Explaining Data-Mixing Scaling Laws

Note
πŸ“„

Fun Resources

Note
πŸ“„

Hand of Straights

Note
πŸ“„

House Robber II

Note
πŸ“„

How Feature Learning can Improve Neural Scaling

Note
πŸ“„

Longest common Prefix and Sorting a String

Note
πŸ“„

Not all solutions are created equal

Note
πŸ“„

Professors and Labs

Note
πŸ“„

Untitled

Note
πŸ“„

Untitled 1

Note
πŸ“„

Untitled 2

Note
πŸ“„

Untitled 3

Note

© 2026 Utsab's Note Repository. Built with Hugo.