diff --git a/src/components/More/index.jsx b/src/components/More/index.jsx index 1f7f2eb..09de6f3 100644 --- a/src/components/More/index.jsx +++ b/src/components/More/index.jsx @@ -18,6 +18,652 @@ import { TopLine, } from "./MoreElements"; +const book = [ + { + text: "Computer vision: models, learning, and inference", + link: "http://computervisionmodels.com", + details: [ + "2012 book published with CUP", + "Focused on probabilistic models", + 'Pre-"deep learning"', + "Lots of ML content", + "Individual chapters available below", + ], + }, +]; + +const transformersAndLLMs = [ + { + text: "Intro to LLMs", + link: "https://www.borealisai.com/research-blogs/a-high-level-overview-of-large-language-models/", + details: [ + "What is an LLM?", + "Pretraining", + "Instruction fine-tuning", + "Reinforcement learning from human feedback", + "Notable LLMs", + "LLMs without training from scratch", + ], + }, + { + text: "Transformers I", + link: "https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/", + details: [ + "Dot-Product self-attention", + "Scaled dot-product self-attention", + "Position encoding", + "Multiple heads", + "Transformer block", + "Encoders", + "Decoders", + "Encoder-Decoders", + ], + }, + { + text: "Transformers II", + link: "https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/", + details: [ + "Sinusoidal position embeddings", + "Learned position embeddings", + "Relatives vs. absolute position embeddings", + "Extending transformers to longer sequences", + "Reducing attention matrix size", + "Making attention matrix sparse", + "Kernelizing attention computation", + "Attention as an RNN", + "Attention as a hypernetwork", + "Attention as a routing network", + "Attention and graphs", + "Attention and convolutions", + "Attention and gating", + "Attention and memory retrieval", + ], + }, + { + text: "Transformers III", + link: "https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/", + details: [ + "Tricks for training transformers", + "Why are these tricks required?", + "Removing layer normalization", + "Balancing residual dependencies", + "Reducing optimizer variance", + "How to train deeper transformers on small datasets", + ], + }, + { + text: "Training and fine-tuning LLMs", + link: "https://www.borealisai.com/research-blogs/training-and-fine-tuning-large-language-models/", + details: [ + "Large language models", + "Pretraining", + "Supervised fine tuning", + "Reinforcement learning from human feedback", + "Direct preference optimization", + ], + }, + { + text: "Speeding up inference in LLMs", + link: "https://www.borealisai.com/research-blogs/speeding-up-inference-in-transformers/", + details: [ + "Problems with transformers", + "Attention-free transformers", + "Complexity", + "RWKV", + "Linear transformers and performers", + "Retentive network", + ], + }, +]; + +const mathForMachineLearning = [ + { + text: "Linear algebra", + link: "https://drive.google.com/file/d/1j2v2n6STPnblOCZ1_GBcVAZrsYkjPYwR/view?usp=sharing", + details: [ + "Vectors and matrices", + "Determinant and trace", + "Orthogonal matrices", + "Null space", + "Linear transformations", + "Singular value decomposition", + "Least squares problems", + "Principal direction problems", + "Inversion of block matrices", + "Schur complement identity", + "Sherman-Morrison-Woodbury", + "Matrix determinant lemma", + ], + }, + { + text: "Introduction to probability", + link: "https://drive.google.com/file/d/1cmxXneW122-hcfmMRjEE-n5C9T2YvuQX/view?usp=sharing", + details: [ + "Random variables", + "Joint probability", + "Marginal probability", + "Conditional probability", + "Bayes' rule", + "Independence", + "Expectation", + ], + }, + { + text: "Probability distributions", + link: "https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing", + details: [ + "Bernouilli distribution", + "Beta distribution", + "Categorical distribution", + "Dirichlet distribution", + "Univariate normal distribution", + "Normal inverse-scaled gamma distribution", + "Multivariate normal distribution", + "Normal inverse Wishart distribution", + "Conjugacy", + ], + }, + { + text: "Fitting probability distributions", + link: "https://drive.google.com/file/d/1DZ4rCmC7AZ8PFc51PiMUIkBO-xqKT_CG/view?usp=sharing", + details: [ + "Maximum likelihood", + "Maximum a posteriori", + "Bayesian approach", + "Example: fitting normal", + "Example: fitting categorical", + ], + }, + { + text: "The normal distribution", + link: "https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing", + details: [ + "Types of covariance matrix", + "Decomposition of covariance", + "Linear transformations", + "Marginal distributions", + "Conditional distributions", + "Product of two normals", + "Change of variable formula", + ], + }, +]; + +const optimization = [ + { + text: "Gradient-based optimization", + link: "https://drive.google.com/file/d/1IoOSfJ0ku89aVyM9qygPl4MVnAhMEbAZ/view?usp=sharing", + details: [ + "Convexity", + "Steepest descent", + "Newton's method", + "Gauss-Newton method", + "Line search", + "Reparameterization", + ], + }, + { + text: "Bayesian optimization", + link: "https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/", + details: [ + "Gaussian processes", + "Acquisition functions", + "Incorporating noise", + "Kernel choice", + "Learning GP parameters", + "Tips, tricks, and limitations", + "Beta-Bernoulli bandit", + "Random forests for BO", + "Tree-Parzen estimators", + ], + }, + { + text: "SAT Solvers I", + link: "https://www.borealisai.com/en/blog/tutorial-9-sat-solvers-i-introduction-and-applications/", + details: [ + "Boolean logic and satisfiability", + "Conjunctive normal form", + "The Tseitin transformation", + "SAT and related problems", + "SAT constructions", + "Graph coloring and scheduling", + "Fitting binary neural networks", + "Fitting decision trees", + ], + }, + { + text: "SAT Solvers II", + link: "https://www.borealisai.com/en/blog/tutorial-10-sat-solvers-ii-algorithms/", + details: [ + "Conditioning", + "Resolution", + "Solving 2-SAT by unit propagation", + "Directional resolution", + "SAT as binary search", + "DPLL", + "Conflict driven clause learning", + ], + }, + { + text: "SAT Solvers III", + link: "https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/", + details: [ + "Satisfiability vs. problem size", + "Factor graph representation", + "Max product / sum product for SAT", + "Survey propagation", + "SAT with non-binary variables", + "SMT solvers", + ], + }, +]; + +const temporalModels = [ + { + text: "Temporal models", + link: "https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing", + details: [ + "Kalman filter", + "Smoothing", + "Extended Kalman filter", + "Unscented Kalman filter", + "Particle filtering", + ], + }, +]; + +const computerVision = [ + { + text: "Image Processing", + link: "https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing", + details: [ + "Whitening", + "Histogram equalization", + "Filtering", + "Edges and corners", + "Dimensionality reduction", + ], + }, + { + text: "Pinhole camera", + link: "https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing", + details: [ + "Pinhole camera model", + "Radial distortion", + "Homogeneous coordinates", + "Learning extrinsic parameters", + "Learning intrinsic parameters", + "Inferring three-dimensional world points", + ], + }, + { + text: "Geometric transformations", + link: "https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing", + details: [ + "Euclidean, similarity, affine, projective transformations", + "Fitting transformation models", + "Inference in transformation models", + "Three geometric problems for planes", + "Transformations between images", + "Robust learning of transformations", + ], + }, + { + text: "Multiple cameras", + link: "https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing", + details: [ + "Two view geometry", + "The essential matrix", + "The fundamental matrix", + "Two-view reconstruction pipeline", + "Rectification", + "Multiview reconstruction", + ], + }, +]; + +const reinforcementLearning = [ + { + text: "Transformers in RL", + link: "https://arxiv.org/abs/2307.05979", + details: [ + "Challenges in RL", + "Advantages of transformers for RL", + "Representation learning", + "Transition function learning", + "Reward learning", + "Policy learning", + "Training strategy", + "Interpretability", + "Applications", + ], + }, +]; + +const aiTheory = [ + { + text: "Gradient flow", + link: "https://www.borealisai.com/research-blogs/gradient-flow/", + details: [ + "Gradient flow", + "Evolution of residual", + "Evolution of parameters", + "Evolution of model predictions", + "Evolution of prediction covariance", + ], + }, + { + text: "Neural tangent kernel", + link: "https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/", + details: [ + "Infinite width neural networks", + "Training dynamics", + "Empirical NTK for shallow network", + "Analytical NTK for shallow network", + "Empirical NTK for deep network", + "Analytical NTK for deep network", + ], + }, + { + text: "NTK applications", + link: "https://www.borealisai.com/research-blogs/neural-tangent-kernel-applications/", + details: [ + "Trainability", + "Convergence bounds", + "Evolution of parameters", + "Evolution of predictions", + "NTK Gaussian processes", + "NTK and generalizability", + ], + }, +]; + +const unsupervisedLearning = [ + { + text: "Modeling complex data densities", + link: "https://drive.google.com/file/d/1BrPHxAuyz28hhz_FtbO0A1cWYdMs2_h8/view?usp=sharing", + details: [ + "Hidden variables", + "Expectation maximization", + "Mixture of Gaussians", + "The t-distribution", + "Factor analysis", + "The EM algorithm in detail", + ], + }, + { + text: "Variational autoencoders", + link: "https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/", + details: [ + "Non-linear latent variable models", + "Evidence lower bound (ELBO)", + "ELBO properties", + "Variational approximation", + "The variational autoencoder", + "Reparameterization trick", + ], + }, + { + text: "Normalizing flows: introduction and review", + link: "https://arxiv.org/abs/1908.09257", + details: [ + "Normalizing flows", + "Elementwise and linear flows", + "Planar and radial flows", + "Coupling and auto-regressive flows", + "Coupling functions", + "Residual flows", + "Infinitesimal (continuous) flows", + "Datasets and performance", + ], + }, +]; + +const graphicalModels = [ + { + text: "Graphical models", + link: "https://drive.google.com/file/d/1ghgeRmeZMyzNHcuzVwS4vRP6BXi3npVO/view?usp=sharing", + details: [ + "Conditional independence", + "Directed graphical models", + "Undirected graphical models", + "Inference in graphical models", + "Sampling in graphical models", + "Learning in graphical models", + ], + }, + { + text: "Models for chains and trees", + link: "https://drive.google.com/file/d/1WAMc3wtZoPv5wRkdF-D0SShVYF6Net84/view?usp=sharing", + details: [ + "Hidden Markov models", + "Viterbi algorithm", + "Forward-backward algorithm", + "Belief propagation", + "Sum product algorithm", + "Extension to trees", + "Graphs with loops", + ], + }, + { + text: "Models for grids", + link: "https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing", + details: [ + "Markov random fields", + "MAP inference in binary pairwise MRFs", + "Graph cuts", + "Multi-label pairwise MRFs", + "Alpha-expansion algorithm", + "Conditional random fields", + ], + }, +]; + +const machineLearning = [ + { + text: "Learning and inference", + link: "https://drive.google.com/file/d/1ArWWi-qbzK2ih6KpOeIF8wX5g3S4J5DY/view?usp=sharing", + details: [ + "Discriminative models", + "Generative models", + "Example: regression", + "Example: classification", + ], + }, + { + text: "Regression models", + link: "https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing", + details: [ + "Linear regression", + "Bayesian linear regression", + "Non-linear regression", + "Bayesian non-linear regression", + "The kernel trick", + "Gaussian process regression", + "Sparse linear regression", + "Relevance vector regression", + ], + }, + { + text: "Classification models", + link: "https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing", + details: [ + "Logistic regression", + "Bayesian logistic regression", + "Non-linear logistic regression", + "Gaussian process classification", + "Relevance vector classification", + "Incremental fitting: boosting and trees", + "Multi-class logistic regression", + ], + }, + { + text: "Few-shot learning and meta-learning I", + link: "https://www.borealisai.com/en/blog/tutorial-2-few-shot-learning-and-meta-learning-i/", + details: [ + "Meta-learning framework", + "Approaches to meta-learning", + "Matching networks", + "Prototypical networks", + "Relation networks", + ], + }, + { + text: "Few-shot learning and meta-learning II", + link: "https://www.borealisai.com/en/blog/tutorial-3-few-shot-learning-and-meta-learning-ii/", + details: [ + "MAML & Reptile", + "LSTM based meta-learning", + "Reinforcement learning based approaches", + "Memory augmented neural networks", + "SNAIL", + "Generative models", + "Data augmentation approaches", + ], + }, +]; + +const nlp = [ + { + text: "Neural natural language generation I", + link: "https://www.borealisai.com/en/blog/tutorial-6-neural-natural-language-generation-decoding-algorithms/", + details: [ + "Encoder-decoder architecture", + "Maximum-likelihood training", + "Greedy search", + "Beam search", + "Diverse beam search", + "Top-k sampling", + "Nucleus sampling", + ], + }, + { + text: "Neural natural language generation II", + link: "https://www.borealisai.com/en/blog/tutorial-7-neural-natural-language-generation-sequence-level-training/", + details: [ + "Fine-tuning with reinforcement learning", + "Training from scratch with RL", + "RL vs. structured prediction", + "Minimum risk training", + "Scheduled sampling", + "Beam search optimization", + "SeaRNN", + "Reward-augmented maximum likelihood", + ], + }, + { + text: "Parsing I", + link: "https://www.borealisai.com/en/blog/tutorial-15-parsing-i-context-free-grammars-and-cyk-algorithm/", + details: [ + "Parse trees", + "Context-free grammars", + "Chomsky normal form", + "CYK recognition algorithm", + "Worked example", + ], + }, + { + text: "Parsing II", + link: "https://www.borealisai.com/en/blog/tutorial-18-parsing-ii-wcfgs-inside-algorithm-and-weighted-parsing/", + details: [ + "Weighted context-free grammars", + "Semirings", + "Inside algorithm", + "Inside weights", + "Weighted parsing", + ], + }, + { + text: "Parsing III", + link: "https://www.borealisai.com/en/blog/tutorial-19-parsing-iii-pcfgs-and-inside-outside-algorithm/", + details: [ + "Probabilistic context-free grammars", + "Parameter estimation (supervised)", + "Parameter estimation (unsupervised)", + "Viterbi training", + "Expectation maximization", + "Outside from inside", + "Interpretation of outside weights", + ], + }, + { + text: "XLNet", + link: "https://www.borealisai.com/en/blog/understanding-xlnet/", + details: [ + "Language modeling", + "XLNet training objective", + "Permutations", + "Attention mask", + "Two stream self-attention", + ], + }, +]; + +const responsibleAI = [ + { + text: "Bias and fairness", + link: "https://www.borealisai.com/en/blog/tutorial1-bias-and-fairness-ai/", + details: [ + "Sources of bias", + "Demographic Parity", + "Equality of odds", + "Equality of opportunity", + "Individual fairness", + "Bias mitigation", + ], + }, + { + text: "Explainability I", + link: "https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/", + details: [ + "Taxonomy of XAI approaches", + "Local post-hoc explanations", + "Individual conditional explanation", + "Counterfactual explanations", + "LIME & Anchors", + "Shapley additive explanations & SHAP", + ], + }, + { + text: "Explainability II", + link: "https://www.borealisai.com/research-blogs/explainability-ii-global-explanations-proxy-models-and-interpretable-models/", + details: [ + "Global feature importance", + "Partial dependence & ICE plots", + "Accumulated local effects", + "Aggregate SHAP values", + "Prototypes & criticisms", + "Surrogate / proxy models", + "Inherently interpretable models", + ], + }, + { + text: "Differential privacy I", + link: "https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/", + details: [ + "Early approaches to privacy", + "Fundamental law of information recovery", + "Differential privacy", + "Properties of differential privacy", + "The Laplace mechanism", + "Examples", + "Other mechanisms and definitions", + ], + }, + { + text: "Differential privacy II", + link: "https://www.borealisai.com/en/blog/tutorial-13-differential-privacy-ii-machine-learning-and-data-generation/", + details: [ + "Differential privacy and matchine learning", + "DPSGD", + "PATE", + "Differentially private data generation", + "DPGAN", + "PateGAN", + ], + }, +]; + export default function MoreSection() { return ( <> @@ -31,13 +677,13 @@ export default function MoreSection() { Other articles, blogs, and books that I have written. Most in a similar style and using the same notation as Understanding Deep - Learning.{" "} + Learning. - Car + More @@ -45,1097 +691,238 @@ export default function MoreSection() { Book -
  • - - Computer vision: models, learning, and inference - - - -
  • 2012 book published with CUP
    • -
  • Focused on probabilistic models
    • -
  • Pre-"deep learning"
    • -
  • Lots of ML content
    • -
  • Individual chapters available below
    • - - - + {book.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Transformers & LLMs -
  • - - Intro to LLMs - - - -
  • What is an LLM?
    • -
  • Pretraining
    • -
  • Instruction fine-tuning
    • -
  • Reinforcement learning from human feedback
    • -
  • Notable LLMs
    • -
  • LLMs without training from scratch
    • - - - -
  • - - Transformers I - - - -
  • Dot-Product self-attention
    • -
  • Scaled dot-product self-attention
    • -
  • Position encoding
    • -
  • Multiple heads
    • -
  • Transformer block
    • -
  • Encoders
    • -
  • Decoders
    • -
  • Encoder-Decoders
    • - - - -
  • - - Transformers II - - - -
  • Sinusoidal position embeddings
    • -
  • Learned position embeddings
    • -
  • Relatives vs. absolute position embeddings
    • -
  • Extending transformers to longer sequences
    • -
  • Reducing attention matrix size
    • -
  • Making attention matrix sparse
    • -
  • Kernelizing attention computation
    • -
  • Attention as an RNN
    • -
  • Attention as a hypernetwork
    • -
  • Attention as a routing network
    • -
  • Attention and graphs
    • -
  • Attention and convolutions
    • -
  • Attention and gating
    • -
  • Attention and memory retrieval
    • - - - -
  • - - Transformers III - - - -
  • Tricks for training transformers
    • -
  • Why are these tricks required?
    • -
  • Removing layer normalization
    • -
  • Balancing residual dependencies
    • -
  • Reducing optimizer variance
    • -
  • - {" "} - How to train deeper transformers on small datasets{" "} -
    • - - - -
  • - - Training and fine-tuning LLMs - - - -
  • Large language models
    • -
  • Pretraining
    • -
  • Supervised fine tuning
    • -
  • Reinforcement learning from human feedback
    • -
  • Direct preference optimization
    • - - - -
  • - - Speeding up inference in LLMs - - - -
  • Problems with transformers
    • -
  • Attention-free transformers
    • -
  • Complexity
    • -
  • RWKV
    • -
  • Linear transformers and performers
    • -
  • Retentive network
    • - - - + {transformersAndLLMs.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Math for machine learning -
  • - - Linear algebra - - - -
  • Vectors and matrices
    • -
  • Determinant and trace
    • -
  • Orthogonal matrices
    • -
  • Null space
    • -
  • Linear transformations
    • -
  • Singular value decomposition
    • -
  • Least squares problems
    • -
  • Principal direction problems
    • -
  • Inversion of block matrices
    • -
  • Schur complement identity
    • -
  • Sherman-Morrison-Woodbury
    • -
  • Matrix determinant lemma
    • - - - -
  • - - Introduction to probability - - - -
  • Random variables
    • -
  • Joint probability
    • -
  • Marginal probability
    • -
  • Conditional probability
    • -
  • Bayes' rule
    • -
  • Independence
    • -
  • Expectation
    • - - - -
  • - - Probability distributions - - - -
  • Bernouilli distribution
    • -
  • Beta distribution
    • -
  • Categorical distribution
    • -
  • Dirichlet distribution
    • -
  • Univariate normal distribution
    • -
  • Normal inverse-scaled gamma distribution
    • -
  • Multivariate normal distribution
    • -
  • Normal inverse Wishart distribution
    • -
  • Conjugacy
    • - - - -
  • - - Fitting probability distributions - - - -
  • Maximum likelihood
    • -
  • Maximum a posteriori
    • -
  • Bayesian approach
    • -
  • Example: fitting normal
    • -
  • Example: fitting categorical
    • - - - -
  • - - The normal distribution - - - -
  • Types of covariance matrix
    • -
  • Decomposition of covariance
    • -
  • Linear transformations
    • -
  • Marginal distributions
    • -
  • Conditional distributions
    • -
  • Product of two normals
    • -
  • Change of variable formula
    • - - - + {mathForMachineLearning.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Optimization -
  • - - Gradient-based optimization - - - -
  • Convexity
    • -
  • Steepest descent
    • -
  • Newton's method
    • -
  • Gauss-Newton method
    • -
  • Line search
    • -
  • Reparameterization
    • - - - -
  • - - Bayesian optimization - - - -
  • Gaussian processes
    • -
  • Acquisition functions
    • -
  • Incorporating noise
    • -
  • Kernel choice
    • -
  • Learning GP parameters
    • -
  • Tips, tricks, and limitations
    • -
  • Beta-Bernoulli bandit
    • -
  • Random forests for BO
    • -
  • Tree-Parzen estimators
    • - - - -
  • - - SAT Solvers I - - - -
  • Boolean logic and satisfiability
    • -
  • Conjunctive normal form
    • -
  • The Tseitin transformation
    • -
  • SAT and related problems
    • -
  • SAT constructions
    • -
  • Graph coloring and scheduling
    • -
  • Fitting binary neural networks
    • -
  • Fitting decision trees
    • - - - -
  • - - SAT Solvers II - - - -
  • Conditioning
    • -
  • Resolution
    • -
  • Solving 2-SAT by unit propagation
    • -
  • Directional resolution
    • -
  • SAT as binary search
    • -
  • DPLL
    • -
  • Conflict driven clause learning
    • - - - -
  • - - SAT Solvers III - - - -
  • Satisfiability vs. problem size
    • -
  • Factor graph representation
    • -
  • Max product / sum product for SAT
    • -
  • Survey propagation
    • -
  • SAT with non-binary variables
    • -
  • SMT solvers
    • - - - + {optimization.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     - {/*
  • - - SAT Solvers III - - - -
  • Satisfiability vs. problem size
    • -
  • Factor graph representation
    • -
  • Max product / sum product for SAT
    • -
  • Survey propagation
    • -
  • SAT with non-binary variables
    • -
  • SMT solvers
    • - - - - - Computer vision - -
  • - - Image Processing - - - -
  • Whitening
    • -
  • Histogram equalization
    • -
  • Filtering
    • -
  • Edges and corners
    • -
  • Dimensionality reduction
    • - - - -
  • - - Pinhole camera - - - -
  • Pinhole camera model
    • -
  • Radial distortion
    • -
  • Homogeneous coordinates
    • -
  • Learning extrinsic parameters
    • -
  • Learning intrinsic parameters
    • -
  • Inferring three-dimensional world points
    • - - - -
  • - - Geometric transformations - - - -
  • - {" "} - Euclidean, similarity, affine, projective - transformations{" "} -
    • */} Temporal models -
  • - - Temporal models - - - -
  • Kalman filter
    • -
  • Smoothing
    • -
  • Extended Kalman filter
    • -
  • Unscented Kalman filter
    • -
  • Particle filtering
    • - - - + {temporalModels.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Computer vision -
  • - - Image Processing - - - -
  • Whitening
    • -
  • Histogram equalization
    • -
  • Filtering
    • -
  • Edges and corners
    • -
  • Dimensionality reduction
    • - - - -
  • - - Pinhole camera - - - -
  • Pinhole camera model
    • -
  • Radial distortion
    • -
  • Homogeneous coordinates
    • -
  • Learning extrinsic parameters
    • -
  • Learning intrinsic parameters
    • -
  • Inferring three-dimensional world points
    • - - - -
  • - - Geometric transformations - - - -
  • - {" "} - Euclidean, similarity, affine, projective - transformations{" "} -
    • -
  • Fitting transformation models
    • -
  • Inference in transformation models
    • -
  • Three geometric problems for planes
    • -
  • Transformations between images
    • -
  • Robust learning of transformations
    • - - - -
  • - - Multiple cameras - - - -
  • Two view geometry
    • -
  • The essential matrix
    • -
  • The fundamental matrix
    • -
  • Two-view reconstruction pipeline
    • -
  • Rectification
    • -
  • Multiview reconstruction
    • - - - + {computerVision.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Reinforcement learning -
  • - - Transformers in RL - - - -
  • Challenges in RL
    • -
  • Advantages of transformers for RL
    • -
  • Representation learning
    • -
  • Transition function learning
    • -
  • Reward learning
    • -
  • Policy learning
    • -
  • Training strategy
    • -
  • Interpretability
    • -
  • Applications
    • - - - + {reinforcementLearning.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     - {/* ########################################### */} - AI Theory -
  • - - Gradient flow - - - -
  • Gradient flow
    • -
  • Evolution of residual
    • -
  • Evolution of parameters
    • -
  • Evolution of model predictions
    • -
  • Evolution of prediction covariance
    • - - - -
  • - - Neural tangent kernel - - - -
  • Infinite width neural networks
    • -
  • Training dynamics
    • -
  • Empirical NTK for shallow network
    • -
  • Analytical NTK for shallow network
    • -
  • Empirical NTK for deep network
    • -
  • Analtical NTK for deep network
    • - - - -
  • - {/* - - Temporal models - - - -
  • Kalman filter
    • -
  • Smoothing
    • -
  • Extended Kalman filter
    • -
  • Unscented Kalman filter
    • -
  • Particle filtering
    • */} - - NTK applications - - - -
  • Trainability
    • -
  • Convergence bounds
    • -
  • Evolution of parameters
    • -
  • Evolution of predictions
    • -
  • NTK Gaussian processes
    • -
  • NTK and generalizability
    • -     -     - + {aiTheory.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     - Unsupervised learning + Unsupervised learning -
  • - - Modeling complex data densities - - - -
  • Hidden variables
    • -
  • Expectation maximization
    • -
  • Mixture of Gaussians
    • -
  • The t-distribution
    • -
  • Factor analysis
    • -
  • The EM algorithm in detail
    • - - - - -
  • - - Variational autoencoders - - - -
  • Non-linear latent variable models
    • -
  • Evidence lower bound (ELBO)
    • -
  • ELBO properties
    • -
  • Variational approximation
    • -
  • The variational autoencoder
    • -
  • Reparameterization trick
    • - - - -
  • - - Normalizing flows: introduction and review - - - -
  • Normalizing flows
    • -
  • Elementwise and linear flows
    • -
  • Planar and radial flows
    • -
  • Coupling and auto-regressive flows
    • -
  • Coupling functions
    • -
  • Residual flows
    • -
  • Infinitesimal (continuous) flows
    • -
  • Datasets and performance
    • - - - + {unsupervisedLearning.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     + Graphical Models -
  • - - Graphical models - - - -
  • Conditional independence
    • -
  • Directed graphical models
    • -
  • Undirected graphical models
    • -
  • Inference in graphical models
    • -
  • Sampling in graphical models
    • -
  • Learning in graphical models
    • - - - -
  • - - Models for chains and trees - - - -
  • Hidden Markov models
    • -
  • Viterbi algorithm
    • -
  • Forward-backward algorithm
    • -
  • Belief propagation
    • -
  • Sum product algorithm
    • -
  • Extension to trees
    • -
  • Graphs with loops
    • - - - -
  • - - Models for grids - - - -
  • Markov random fields
    • -
  • MAP inference in binary pairwise MRFs
    • -
  • Graph cuts
    • -
  • Multi-label pairwise MRFs
    • -
  • Alpha-expansion algorithm
    • -
  • Conditional random fields
    • - - - + {graphicalModels.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Machine learning -
  • - - Learning and inference - - - -
  • Discriminative models
    • -
  • Generative models
    • -
  • Example: regression
    • -
  • Example: classification
    • - - - -
  • - - Regression models - - - -
  • Linear regression
    • -
  • Bayesian linear regression
    • -
  • Non-linear regression
    • -
  • Bayesian non-linear regression
    • -
  • The kernel trick
    • -
  • Gaussian process regression
    • -
  • Sparse linear regression
    • -
  • Relevance vector regression
    • - - - -
  • - - Classification models - - - -
  • Logistic regression
    • -
  • Bayesian logistic regression
    • -
  • Non-linear logistic regression
    • -
  • Gaussian process classification
    • -
  • Relevance vector classification
    • -
  • Incremental fitting: boosting and trees
    • -
  • Multi-class logistic regression
    • - - - -
  • - - Few-shot learning and meta-learning I - - - -
  • Meta-learning framework
    • -
  • Approaches to meta-learning
    • -
  • Matching networks
    • -
  • Prototypical networks
    • -
  • Relation networks
    • - - - -
  • - - Few-shot learning and meta-learning II - - - -
  • MAML & Reptile
    • -
  • LSTM based meta-learning
    • -
  • Reinforcement learning based approaches
    • -
  • Memory augmented neural networks
    • -
  • SNAIL
    • -
  • Generative models
    • -
  • Data augmentation approaches
    • - - - + {machineLearning.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Natural language processing -
  • - - Neural natural language generation I - - - -
  • Encoder-decoder architecture
    • -
  • Maximum-likelihood training
    • -
  • Greedy search
    • -
  • Beam search
    • -
  • Diverse beam search
    • -
  • Top-k sampling
    • -
  • Nucleus sampling
    • - - - -
  • - - Neural natural language generation II - - - -
  • Fine-tuning with reinforcement learning
    • -
  • Training from scratch with RL
    • -
  • RL vs. structured prediction
    • -
  • Minimum risk training
    • -
  • Scheduled sampling
    • -
  • Beam search optimization
    • -
  • SeaRNN
    • -
  • Reward-augmented maximum likelihood
    • - - - -
  • - - Parsing I - - - -
  • Parse trees
    • -
  • Context-free grammars
    • -
  • Chomsky normal form
    • -
  • CYK recognition algorithm
    • -
  • Worked example
    • - - - -
  • - - Parsing II - - - -
  • Weighted context-free grammars
    • -
  • Semirings
    • -
  • Inside algorithm
    • -
  • Inside weights
    • -
  • Weighted parsing
    • - - - -
  • - - Parsing III - - - -
  • Probabilistic context-free grammars
    • -
  • Parameter estimation (supervised)
    • -
  • Parameter estimation (unsupervised)
    • -
  • Viterbi training
    • -
  • Expectation maximization
    • -
  • Outside from inside
    • -
  • Interpretation of outside weights
    • - - - -
  • - - XLNet - - - -
  • Language modeling
    • -
  • XLNet training objective
    • -
  • Permutations
    • -
  • Attention mask
    • -
  • Two stream self-attention
    • - - - + {nlp.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}     Responsible AI -
  • - - Bias and fairness - - - -
  • Sources of bias
    • -
  • Demographic Parity
    • -
  • Equality of odds
    • -
  • Equality of opportunity
    • -
  • Individual fairness
    • -
  • Bias mitigation
    • - - - -
  • - - Explainability I - - - -
  • Taxonomy of XAI approaches
    • -
  • Local post-hoc explanations
    • -
  • Individual conditional explanation
    • -
  • Counterfactual explanations
    • -
  • LIME & Anchors
    • -
  • Shapley additive explanations & SHAP
    • - - - -
  • - - Explainability II - - - -
  • Global feature importance
    • -
  • Partial dependence & ICE plots
    • -
  • Accumulated local effects
    • -
  • Aggregate SHAP values
    • -
  • Prototypes & criticisms
    • -
  • Surrogate / proxy models
    • -
  • Inherently interpretable models
    • - - - -
  • - - Differential privacy I - - - -
  • Early approaches to privacy
    • -
  • Fundamental law of information recovery
    • -
  • Differential privacy
    • -
  • Properties of differential privacy
    • -
  • The Laplace mechanism
    • -
  • Examples
    • -
  • Other mechanisms and definitions
    • - - - -
  • - - Differential privacy II - - - -
  • Differential privacy and matchine learning
    • -
  • DPSGD
    • -
  • PATE
    • -
  • Differentially private data generation
    • -
  • DPGAN
    • -
  • PateGAN
    • - - - + {responsibleAI.map((item, index) => ( +
  • + + {item.text} + + + + {item.details.map((detail, index) => ( +
  • {detail}
    • + ))} + + + + ))}