🎓How I Study AIHISA
📖Read
📄Papers📰Blogs🎬Courses
💡Learn
🛤️Paths📚Topics💡Concepts🎴Shorts
🎯Practice
⏱️Coach🧩Problems🧠Thinking🎯Prompts🧠Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts14

Groups

📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

Category

🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginnerIntermediate
⚙️AlgorithmIntermediate

Mixed Precision Training

Mixed precision training stores and computes tensors in low precision (FP16/BF16) for speed and memory savings while keeping a master copy of weights in FP32 for accurate updates.

#mixed precision#fp16#bf16+10
⚙️AlgorithmIntermediate

Distributed & Parallel Optimization

Data parallelism splits the training data across workers that compute gradients in parallel on a shared model.

#data parallelism
12
Advanced
Group:
Optimization
#synchronous sgd
#asynchronous sgd
+12
⚙️AlgorithmIntermediate

Lion Optimizer

Lion (Evolved Sign Momentum) is a first-order, sign-based optimizer discovered through automated program search.

#lion optimizer#sign-based optimization#momentum+12
⚙️AlgorithmIntermediate

Sharpness-Aware Minimization (SAM)

Sharpness-Aware Minimization (SAM) trains models to perform well even when their weights are slightly perturbed, seeking flatter minima that generalize better.

#sharpness-aware minimization#sam optimizer#robust optimization+11
📚TheoryIntermediate

Loss Landscape Analysis

A loss landscape is the “terrain” of a model’s loss as you move through parameter space; valleys are good solutions and peaks are bad ones.

#loss landscape#sharpness#hessian eigenvalues+12
📚TheoryIntermediate

Weight Initialization Strategies

Weight initialization sets the starting values of neural network parameters so signals and gradients neither explode nor vanish as they pass through layers.

#xavier#glorot#he+12
⚙️AlgorithmIntermediate

Gradient Clipping & Normalization

Gradient clipping limits how large gradient values or their overall magnitude can become during optimization to prevent exploding updates.

#gradient clipping#clipping by norm#clipping by value+12
⚙️AlgorithmAdvanced

Newton's Method & Second-Order Optimization

Newton's method uses both the gradient and the Hessian to take steps that aim directly at the local optimum by fitting a quadratic model of the loss around the current point.

#newton's method#second-order optimization#hessian+12
∑MathIntermediate

Lagrange Multipliers & Constrained Optimization

Lagrange multipliers let you optimize a function while strictly satisfying equality constraints by introducing auxiliary variables (the multipliers).

#lagrange multipliers#constrained optimization#kkt conditions+11
⚙️AlgorithmIntermediate

Learning Rate Schedules

Learning rate schedules control how fast a model learns over time by changing the learning rate across iterations or epochs.

#learning rate schedules#step decay#cosine annealing+12
⚙️AlgorithmIntermediate

Adam & Adaptive Methods

Adam is an optimization algorithm that combines momentum (first moment) with RMSProp-style adaptive learning rates (second moment).

#adam#adaptive methods#rmsprop+12
⚙️AlgorithmIntermediate

Momentum Methods

Momentum methods add an exponentially weighted memory of past gradients to make descent steps smoother and faster, especially in ravines and ill-conditioned problems.

#momentum#heavy-ball#polyak momentum+12