Andrew Fairless, Ph.D.
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Entries tagged :: repost
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2025-08-18
What I Read: Convolutions, Polynomials
2025-08-14
What I Read: Scale
2025-08-13
What I Read: Prompt Optimization
2025-08-12
What I Read: JAX, P-splines
2025-08-11
What I Read: Reservoir Sampling
2025-08-07
What I Read: database, scale
2025-08-06
What I Read: Poisson distribution
2025-08-05
What I Read: Python Polars
2025-08-04
What I Read: Learning Agentic Patterns
2025-07-31
What I Read: AGI not milestone
2025-07-30
What I Read: Automatic Sparse Differentiation
2025-07-29
What I Read: Scaling RL
2025-07-28
What I Read: Save Product
2025-07-24
What I Read: AI Products
2025-07-23
What I Read: Generative, latent
2025-07-22
What I Read: BM25F
2025-07-21
What I Read: Language Models
2025-07-17
What I Read: Iceberg, Lance
2025-07-16
What I Read: Dice, Queues
2025-07-15
What I Read: reasoning research
2025-07-14
What I Read: Bayesian Superiority, R2D2 Priors
2025-07-10
What I Read: Clickhouse Clusters
2025-07-09
What I Read: RL Traffic Smoothing
2025-07-08
What I Read: Quarter, Thoughts
2025-07-07
What I Read: Polynomial
2025-07-02
What I Read: Generative AI
2025-07-01
What I Read: LLMOps, human
2025-06-30
What I Read: Cybernetic Teammate
2025-06-26
What I Read: Recommendation, LLMs
2025-06-25
What I Read: Newton update
2025-06-24
What I Read: Small Language Models
2025-06-23
What I Read: LLM Agents
2025-06-19
What I Read: LLM Reasoning
2025-06-18
What I Read: Domain specific architectures
2025-06-17
What I Read: Conditional Flow Matching
2025-06-16
What I Read: multiclass learning
2025-06-12
What I Read: reinforcement learning
2025-06-11
What I Read: LLMs in medicine
2025-06-10
What I Read: Science-Literate AI
2025-06-09
What I Read: TrueSkill
2025-06-05
What I Read: Distributed Systems Programming
2025-06-04
What I Read: composable data platforms
2025-06-03
What I Read: Model, Product
2025-06-02
What I Read: Model calibration
2025-05-29
What I Read: gamma hurdle distribution
2025-05-28
What I Read: BAML
2025-05-27
What I Read: BAML, agentic
2025-05-26
What I Read: RL, PPO, GRPO
2025-05-22
What I Read: Group relative policy optimization
2025-05-21
What I Read: reasoning LLMs
2025-05-20
What I Read: individual risk
2025-05-19
What I Read: chatbot limitations
2025-05-15
What I Read: next weak learners
2025-05-14
What I Read: S3, age
2025-05-13
What I Read: history, tidyverse
2025-05-12
What I Read: AI-ready data
2025-05-08
What I Read: short case, Nvidia
2025-05-07
What I Read: variational autoencoder
2025-05-06
What I Read: commonality analysis
2025-05-05
What I Read: single-node processing
2025-05-01
What I Read: memorization, novelty
2025-04-30
What I Read: adaptive LLM
2025-04-29
What I Read: tensor dimensions, transformers
2025-04-28
What I Read: building AI
2025-04-27
What I Read: cosine similarity
2025-04-24
What I Read: AI HCI
2025-04-23
What I Read: AI languages
2025-04-22
What I Read: statistical intuitions
2025-04-21
What I Read: transfer learning
2025-04-17
What I Read: model merging
2025-04-16
What I Read: optimizing softmax
2025-04-15
What I Read: Agents
2025-04-14
What I Read: Age of Data
2025-04-10
What I Read: ScyllaDB
2025-04-09
What I Read: Building agents
2025-04-08
What I Read: Shapley Interactions
2025-04-07
What I Read: Pooling, Graph Neural Networks
2025-04-03
What I Read: ML, Go
2025-04-02
What I Read: Implementing Raft
2025-04-01
What I Read: reward hacking
2025-03-31
What I Read: data engineering
2025-03-27
What I Read: diffusion flow
2025-03-26
What I Read: Bayesian mixed models
2025-03-25
What I Read: autoencoders, interpretability
2025-03-20
What I Read: simulations, chaos testing
2025-03-19
What I Read: polars, pandas
2025-03-18
What I Read: flow
2025-03-17
What I Read: replacements
2025-03-13
What I Read: ship projects
2025-03-12
What I Read: Incremental Jobs, Data Quality
2025-03-11
What I Read: Declarative Data Stack
2025-03-10
What I Read: quiz, Bayesian
2025-03-06
What I Read: multimodal LLMs
2025-03-05
What I Read: LLMs, school math
2025-03-04
What I Read: gpu computing
2025-03-03
What I Read: debate, ai
2025-02-27
What I Read: shape, matrix
2025-02-26
What I Read: benchmark
2025-02-25
What I Read: llm judge
2025-02-24
What I Read: degree certainty
2025-02-20
What I Read: probability generating
2025-02-19
What I Read: data migrations
2025-02-18
What I Read: myths, random
2025-02-17
What I Read: sampling, SQL
2025-02-13
What I Read: Gaussians
2025-02-12
What I Read: evaluation quicksand
2025-02-11
What I Read: Mamba, State Space Models
2025-02-10
What I Read: Unit Disk Sampling
2025-02-06
What I Read: Multi Objective Optimisation
2025-02-05
What I Read: cosine similarity
2025-02-04
What I Read: Bounded Kernel Density Estimation
2025-02-03
What I Read: uv, Python, production
2025-01-29
What I Read: Statistical Validity, Clusters
2025-01-28
What I Read: Steins Paradox
2025-01-27
What I Read: Transformers Inference Optimization
2025-01-23
What I Read: Viola Jones Algorithm
2025-01-22
What I Read: Data Missing
2025-01-21
What I Read: GenAI, Classify Text
2025-01-16
What I Read: Partial Functions
2025-01-15
What I Read: Jensen’s Inequality
2025-01-13
What I Read: Hampel Filter, time series
2025-01-09
What I Read: LLMs, 2024
2025-01-08
What I Read: Neural Networks, Understandable
2025-01-07
What I Read: sphering transform
2025-01-06
What I Read: embedding models
2024-12-19
What I Read: Toy Models, Superposition?
2024-12-18
What I Read: Is SHAP doomed?
2024-12-17
What I Watch: LLM agents, production
2024-12-16
What I Watch: How LLMs store facts
2024-12-12
What I Watch: compare high dimensional vectors
2024-12-11
What I Read: Fine Tuning LLM
2024-12-10
What I Read: Future of Distributed Systems
2024-12-05
What I Read: data catalogs
2024-12-04
What I Read: passively learned, causality
2024-12-03
What I Read: Evaluating LLM-Evaluators
2024-12-02
What I Read: sparsity, PyTorch, Hadamard product
2024-11-25
What I Read: tilted loss
2024-11-21
What I Read: Classifying pdfs
2024-11-20
What I Read: Tool Retrieval, RAG
2024-11-18
What I Read: LLM Pre-training Post-training
2024-11-14
What I Read: Difference, Statements and Expressions
2024-11-13
What I Read: Open-endedness, Agentic AI
2024-11-12
What I Read: Turing Test, intelligence
2024-11-07
What I Read: Regularization, polynomial bases
2024-11-05
What I Read: Contextual Bandit, LinUCB
2024-11-04
What I Read: Big Data is Dead
2024-10-30
What I Read: Visual Guide, Quantization
2024-10-29
What I Read: Generative AI Platform
2024-10-28
What I Read: History, Transformer
2024-10-24
What I Read: Kernel, Convolutional Representations
2024-10-22
What I Read: standard error
2024-10-21
What I Read: LLM evaluation
2024-10-17
What I Read: Data Flywheels, LLM
2024-10-16
What I Read: Use-cases, inverted PCA
2024-10-15
What I Read: Improving Language Models, Practical Size
2024-10-10
What I Read: Hidden Infinity, Preference Learning
2024-10-09
What I Read: Illustrated AlphaFold
2024-10-07
What I Read: Extrinsic Hallucinations, LLMs
2024-10-03
What I Read: decision analysis, significance testing
2024-10-01
What I Read: What can LLMs never do?
2024-09-30
What I Read: Sliding Window Attention
2024-09-25
What I Read: bare metal to 70B
2024-09-24
What I Read: What’s Fair, What’s Hard
2024-09-23
What I Read: Detecting hallucinations, LLMs, semantic entropy
2024-09-19
What I Read: Structured Generation, LLMs
2024-09-18
What I Read: AI Engineers, Search
2024-09-16
What I Read: Musings on AI Engineering
2024-09-12
What I Read: SSD, Database
2024-09-10
What I Read: Command-line Tools, Faster
2024-09-09
What I Read: Scaling to Multi-Terabyte Datasets
2024-09-05
What I Read: notebooks, McDonalds of code
2024-09-04
What I Read: LLMs train LLMs
2024-09-03
What I Read: Summarization, LLMs
2024-08-29
What I Read: Logarithms, Heteroskedasticity
2024-08-27
What I Read: string ufuncs, NumPy 2.0
2024-08-26
What I Read: neural systems understanding
2024-08-22
What I Read: Cookiecutter Data Science V2
2024-08-21
What I Read: KL All You Need
2024-08-19
What I Read: What We Learned Building LLMs
2024-08-15
What I Read: Merge Large Language Models
2024-08-14
What I Read: Transformers by Hand
2024-08-13
What I Read: LLM Pipelines, DSPy
2024-08-07
What I Read: LLM evaluation
2024-08-06
What I Read: LLM, DSPy Assertions and Suggestions
2024-08-05
What I Read: implicit biases, LLM
2024-07-31
What I Read: AI, Light-Based Chips
2024-07-30
What I Read: Labeling, Uncertainty Sampling
2024-07-29
What I Read: Platonic Hypothesis
2024-07-25
What I Read: Game Theory, AI
2024-07-24
What I Read: StandardScaler
2024-07-23
What I Read: PostgreSQL
2024-07-18
What I Read: time series, Gaussian processes
2024-07-17
What I Read: Matryoshka Embedding
2024-07-15
What I Read: LLMs, Open Source
2024-07-10
What I Read: anti-patterns, data reuse
2024-07-09
What I Read: Hallucinations, AI
2024-07-08
What I Read: Ring Attention
2024-07-02
What I Read: Kalman Filter
2024-07-01
What I Read: Data Science, Mostly Dispatch
2024-06-27
What I Read: Dont put notebooks into production
2024-06-26
What I Read: Flow Matching
2024-06-25
What I Read: How Machines ‘Grok’ Data
2024-06-20
What I Read: Structured Generation, Constrained Decoding
2024-06-18
What I Read: Attention, transformers
2024-06-17
What I Read: Linear Algebra, Random
2024-06-13
What I Read: Generalized Additive Models
2024-06-12
What I Read: Data Selection, LLMs
2024-06-10
What I Read: Mamba Explained
2024-06-05
What I Read: speed up code, Numba
2024-06-04
What I Read: binary vector search
2024-06-03
What I Read: Chain-of-Thought Reasoning
2024-05-29
What I Read: predicate pushdown
2024-05-28
What I Read: Cloud native data loaders ML
2024-05-27
What I Read: Modals web infrastructure
2024-05-23
What I Read: Lincoln, confidence intervals
2024-05-22
What I Read: High-Dimensional Variance
2024-05-20
What I Read: text embeddings
2024-05-15
What I Read: reliance on AI-assisted decisions
2024-05-14
What I Read: 1-bit LLMs, 1.58 Bits
2024-05-13
What I Read: diffusion distillation
2024-05-09
What I Read: Mamba, Easy Way
2024-05-08
What I Read: How fast process CSV file
2024-05-07
What I Read: impute income, opinion polls
2024-05-01
What I Read: Mamba
2024-04-30
What I Read: Structured State Space Sequence Models
2024-04-29
What I Read: Forgetting Can Help AI Learn
2024-04-25
What I Read: Predictive Human Preference, Model Ranking to Model Routing
2024-04-23
What I Read: Diffusion Model theory
2024-04-22
What I Read: Compound AI Systems
2024-04-18
What I Read: Scaling ChatGPT, Engineering Challenges
2024-04-17
What I Read: High-Quality Human Data
2024-04-15
What I Read: Probabilistic Linkage, Data Deduplication
2024-04-11
What I Read: Every infrastructure decision I endorse, regret
2024-04-10
What I Read: How Quickly LLMs Learn Skills?
2024-04-09
What I Read: Deploy Model
2024-04-08
What I Read: Diffusion models, new theoretical perspective
2024-04-04
What I Read: LoRA from Scratch
2024-04-03
What I Read: LLM Evaluation Metrics
2024-04-02
What I Read: Open Source AI
2024-04-01
What I Read: Artificial, Biological Intelligence
2024-03-28
What I Read: SQL order
2024-03-27
What I Read: Database Disassembly
2024-03-26
What I Read: polynomial monster
2024-03-25
What I Read: polynomial features
2024-03-21
What I Read: misleading GPU, CPU benchmarks
2024-03-19
What I Read: Sampling Text Generation
2024-03-18
What I Read: Chatbots Understand Text
2024-03-14
What I Read: Confidence intervals, balanced accuracy
2024-03-13
What I Read: Smooth Noisy Data
2024-03-12
What I Read: Safety, Autonomous Vehicles
2024-03-06
What I Read: Deep learning, single-cell sequencing
2024-03-05
What I Read: Salmon, Loop
2024-03-04
What I Read: Self-Attention in GPT
2024-02-28
What I Read: Navigating Data Tensions
2024-02-27
What I Read: Random Forests, Estimation of Information-Theoretic Quantities
2024-02-26
What I Read: Type Checking
2024-02-22
What I Read: Inducing hierarchy for multi-class classification
2024-02-21
What I Read: Research Directions
2024-02-19
What I Read: Instruction Tuning
2024-02-15
What I Read: Will Scaling Solve Robotics?
2024-02-14
What I Read: 3D human pose estimation
2024-02-13
What I Read: Limits of Transformers on Compositionality
2024-02-08
What I Read: survey LLM tooling
2024-02-07
What I Read: Multi-Modal Retrieval-Augmented Generation
2024-02-06
What I Read: Adversarial Attacks on LLMs
2024-02-05
What I Read: Finetuning LLMs Using LoRA
2024-01-31
What I Read: Learning JAX
2024-01-30
What I Read: Nvidia, GPU gold rush
2024-01-29
What I Read: Gaussian Processes Extrapolate
2024-01-25
What I Read: SciPy builds for Python 3.12
2024-01-24
What I Read: Density Kernel Depth for Outlier Detection
2024-01-23
What I Read: Helping AI See
2024-01-18
What I Read: Unify Batch and ML Systems
2024-01-16
What I Read: vectorize wide PyTorch expressions
2024-01-15
What I Read: GPU Computing
2024-01-11
What I Read: Enterprise AI, RAG + Fine Tuning
2024-01-10
What I Read: Compiling NumPy
2024-01-09
What I Read: Neural algorithmic reasoning
2024-01-08
What I Read: SAT Solvers
2024-01-04
What I Read: Multimodality
2024-01-03
What I Read: Finetuning LLMs with LoRA and QLoRA
2023-12-21
What I Read: Understanding Moments
2023-12-20
What I Read: Distributed Training, Finetuning
2023-12-19
What I Read: Artificial General Intelligence
2023-12-14
What I Read: data integration
2023-12-13
What I Read: Retrieval Augmented Generation at scale
2023-12-12
What I Read: AI System Beats Chess Puzzles
2023-12-11
What I Read: Tiny Language Models
2023-12-07
What I Read: LLM Apps, Data Pipelines
2023-12-06
What I Read: Computational Power, AI
2023-12-05
What I Read: evaluating AI systems
2023-12-04
What I Read: Problems of AI Consciousness
2023-11-30
What I Read: Visualizing Matrix Multiplication
2023-11-28
What I Read: Data, The Land DevOps Forgot
2023-11-27
What I Read: Privacy side channels in ML
2023-11-16
What I Read: Estimate Token Importance in LLM Prompts
2023-11-15
What I Read: AI’s $200B Question
2023-11-14
What I Read: Auditing AI, How Much Access
2023-11-09
What I Read: Overton Paradox
2023-11-08
What I Read: How make history with LLMs
2023-11-06
What I Read: Optimizing LLM in production
2023-11-02
What I Read: Features Are Important?
2023-10-31
What I Read: Physical Process, Generative AI
2023-10-30
What I Read: LLM Training, RLHF
2023-10-25
What I Read: Nvidia AI Supremacy Temporary
2023-10-24
What I Read: Markov Chains
2023-10-23
What I Read: LLMs, single example
2023-10-19
What I Read: Composable Data Systems
2023-10-18
What I Read: Differentiable Trees
2023-10-16
What I Read: To Understand Transformers, Focus on Attention
2023-10-13
What I Read: A.I. Tool Diagnoses Brain Tumors
2023-10-12
What I Read: GPT-4, 8 Models in One
2023-10-11
What I Read: AI, Images
2023-10-10
What I Read: LLM research
2023-10-05
What I Read: Multimodal, Embeddings
2023-10-03
What I Read: Bonsai Networks, RNNs
2023-10-02
What I Read: Models Memorize or Generalize?
2023-09-28
What I Read: scaling laws, cross-entropy loss
2023-09-27
What I Read: Giant Steps Can Solve Optimization Faster
2023-09-25
What I Read: LLMs in Planning
2023-09-21
What I Read: Economic Case for Generative AI
2023-09-20
What I Read: Multiple Imputation by Chained Equations
2023-09-19
What I Read: LLM-based Products
2023-09-13
What I Read: Attention Off By One
2023-09-12
What I Read: Few-Shot Learning
2023-09-11
What I Read: What Do LLMs Know About Linguistics?
2023-09-07
What I Read: LLMs
2023-09-06
What I Read: Accelerating PyTorch
2023-09-05
What I Read: big storage system called S3
2023-08-30
What I Read: Perspectives on diffusion
2023-08-29
What I Read: shape of AGI
2023-08-28
What I Read: What we dont talk about
2023-08-24
What I Read: speed up Python with Rust
2023-08-23
What I Read: Topological Data Analysis
2023-08-21
What I Read: Disagreement Modelling
2023-08-17
What I Read: LLM Agents
2023-08-16
What I Read: artificial intelligence really hard
2023-08-15
What I Read: When NumPy slow
2023-08-09
What I Read: missing data mechanisms
2023-08-08
What I Read: Ways Digital Minds Know
2023-08-07
What I Read: Systems of Intelligence, defensible business model
2023-08-04
What I Read: Attack Impacts AI Chatbots
2023-08-03
What I Read: Neural Networks, Data, Fake
2023-08-02
What I Read: Why Most Data Projects Fail
2023-08-01
What I Read: speed up Numba and NumPy
2023-07-31
What I Read: Sparse Networks
2023-07-27
What I Read: LLM Chatbots, Browser
2023-07-26
What I Read: Neural Networks Learn Language
2023-07-25
What I Read: Kubernetes, Batch
2023-07-20
What I Read: data drift
2023-07-19
What I Read: Hard Stuff, Building Products, LLMs
2023-07-17
What I Read: Prompt injection
2023-07-12
What I Read: What, Why ChatGPT
2023-07-11
What I Read: In-Context Learning
2023-07-10
What I Read: AIs producing own training data
2023-07-06
What I Read: Scaling Up
2023-07-05
What I Read: Natural Language, supply chains
2023-06-29
What I Read: Against LLM
2023-06-28
What I Read: Chatbots, What Isn’t
2023-06-27
What I Read: Reinforcement Learning from Human Feedback
2023-06-21
What I Read: Reinforcement Learning, Language Models
2023-06-20
What I Read: Computation, Artificial Intelligence
2023-06-19
What I Read: Tree-Structured Parzen Estimator
2023-06-15
What I Read: Drawing Neural Networks
2023-06-13
What I Read: Open Source, AlphaTensor
2023-06-12
What I Read: Multi-label NLP
2023-06-08
What I Read: Unsupervised Learning Metrics
2023-06-07
What I Read: different meanings of p-value
2023-06-05
What I Read: One Large Model
2023-05-31
What I Read: smaller LLMs, more tokens
2023-05-30
What I Read: Few Shot, Recommenders, LLMs
2023-05-29
What I Read: LLM applications, production
2023-05-25
What I Read: Prompt Engineering
2023-05-24
What I Read: Multimodal Models
2023-05-23
What I Read: human touch, LLMs
2023-05-18
What I Read: MLOps, Data Engineering
2023-05-17
What I Read: Graph Neural Networks
2023-05-15
What I Read: Topic Modeling
2023-05-11
What I Read: GPT, Ranking
2023-05-09
What I Read: Competitive Machine Learning
2023-05-08
What I Read: Abilities Emerging From AI
2023-05-03
What I Read: Teach Machines to Be Fair
2023-05-02
What I Read: databases
2023-05-01
What I Read: Functional, Object Oriented Programming
2023-04-27
What I Read: Neural Networks, Locks
2023-04-26
What I Read: Bloom filter
2023-04-25
What I Read: Data science, cloud
2023-04-20
What I Read: data, product teams
2023-04-18
What I Read: Relative representations
2023-04-17
What I Read: Predict Distributions
2023-04-12
What I Read: Infrastructure
2023-04-11
What I Learn: neural scaling, data pruning
2023-04-10
What I Read: Geometric Deep Learning
2023-04-06
What I Read: Teach Computers Math
2023-04-05
What I Read: Data Product vs. Service
2023-04-04
What I Learn: SQL, Malloy
2023-03-30
What I Read: Language world models or surface statistics?
2023-03-29
What I Read: More Flexible Machine Learning
2023-03-27
What I Read: SQL pipelines
2023-03-23
What I Read: recommender system architectures
2023-03-21
What I Read: Machines Learn, Teach Basics
2023-03-20
What I Read: Data Normal
2023-03-16
What I Read: Transformer Inference Optimization
2023-03-15
What I Read: Optimizing Machine Learning Training Pipelines
2023-03-14
What I Read: Feature Platforms
2023-03-09
What I Read: Compare Two Ranked Lists
2023-03-08
What I Read: Data Pipeline Design Patterns
2023-03-06
What I Read: Modern AI Art
2023-03-02
What I Read: Shapley Values
2023-03-01
What I Read: Build vs. Buy, Modern Data Stack
2023-02-27
What I Read: Realtime User Actions in Recommendation
2023-02-23
What I Read: Building "Copilot for X"
2023-02-22
What I Read: AI, Human Values
2023-02-21
What I Read: Offline RL, Large Language Models
2023-02-20
What I Read: Data Engineering 2023 Predictions
2023-02-16
What I Read: Realtime ML
2023-02-15
What I Read: ELT Schedules, Root Cause Analysis
2023-02-13
What I Read: Convolutions, Probability
2023-02-09
What I Read: Biases, Saliency
2023-02-07
What I Read: ML Observability
2023-02-06
What I Read: Causal Confounds, Sequential Decision
2023-02-01
What I Read: Realtime ML Pipelines
2023-01-31
What I Read: Machine Learning, Not Like Brain
2023-01-30
What I Read: Matrix Multiplication
2023-01-26
What I Learn: video quality, neural networks
2023-01-24
What I Learn: Simplest Data Pipeline
2023-01-23
What I Learn: Preferences in Recommender Systems
2023-01-19
What I Read: Transformers Training
2023-01-18
What I learn: How, learn machine learning
2023-01-17
What I Read: Learning to Imitate
2023-01-12
What I Read: Federated, Protects Privacy
2023-01-11
What I Read: New Chip, AI
2023-01-09
What I Read: large language model, UX
2023-01-05
What I Read: Poisson Flow Generative Models
2023-01-04
What I Read: Data Pipeline Smoke Tests
2023-01-03
What I Read: Russian Roulette
2022-12-22
What I Read: The Farama Foundation
2022-12-21
What I Read: Pre-Trained Models, Robotics
2022-12-20
What I Read: undesired goals
2022-12-14
What I Read: Bayesian Structural Timeseries
2022-12-13
What I Read: Dev and Data Science Independence
2022-12-12
What I Read: Speech Recognition Metrics
2022-12-08
What I Read: How dbt fails
2022-12-07
What I Read: Sins, Numerical Linear Algebra
2022-12-05
What I Read: Key-Value Databases
2022-12-01
What I Read: Data Engineers, What’s the profession about
2022-11-30
What I Read: How diffusion models work
2022-11-29
What I Read: Illustrated Stable Diffusion
2022-11-28
What I Read: data catalogs, metadata
2022-11-22
What I Read: SQLite
2022-11-21
What I Read: explainability, survival analysis
2022-11-17
What I Read: Generative AI
2022-11-16
What I Read: Productizing Large Language Models
2022-11-15
What I Read: Career in NLP
2022-11-10
What I Read: Chaos Researchers Can Now Predict
2022-11-09
What I Read: Transformers, Brain
2022-11-07
What I Read: end-to-end, infrastructure, recommendations
2022-11-02
What I Read: Neural Tangent Kernel
2022-11-01
What I Read: ML Engineering
2022-10-31
What I Read: deliberately create data
2022-10-27
What I Read: ML, Engagement, Maternal and Child Health
2022-10-25
What I Read: Causal Inference
2022-10-24
What I Read: Machine Learning Metadata
2022-10-20
What I Read: AI Researcher, Bitter Medicine
2022-10-19
What I Read: Structural pattern matching, Python
2022-10-18
What I Read: Zero-Shot, K-Shot Learning
2022-10-13
What I Read: Backpropagation, Chain Rule
2022-10-12
What I Read: AI, Limits, Language
2022-10-11
What I Read: Snowflake Query Optimizer
2022-10-10
What I Read: When use Bayesian optimization
2022-10-06
What I Read: Emergent Features
2022-10-04
What I Read: Self-Taught AI, Brain
2022-10-03
What I Read: Comparing quantiles at scale
2022-09-29
What I Read: Testing Firefox, machine learning
2022-09-28
What I Read: Robot Learned, Scraping Web
2022-09-26
What I Read: Concept Drift Without Labeled Data
2022-09-21
What I Read: streaming for data scientists
2022-09-20
What I Read: Challenging AI to Learn Better
2022-09-19
What I Read: introduction to NumPyro
2022-09-15
What I Read: Supercharging A/B Testing
2022-09-14
What I Read: data-driven companies
2022-09-12
What I Read: BLOOM Training
2022-09-07
What I Read: Mistakes, Recommendation System
2022-09-06
What I Read: Transformers in computer vision
2022-09-05
What I Read: Is Data Scientist Still the Sexiest Job?
2022-09-01
What I Read: Short Time Series
2022-08-31
What I Read: Large Language Models
2022-08-29
What I Read: How Find Great Developers
2022-08-25
What I Read: Anomaly Detection Metrics
2022-08-23
What I Read: Estimating Model Performance
2022-08-17
What I Read: Pandas Anti-Patterns
2022-08-16
What I Read: cross-validation
2022-08-15
What I Read: Hidden Technical Debts
2022-08-11
What I Read: Mimicry, Artificial Intelligence
2022-08-08
What I Read: DALL·E 2, Explained
2022-08-04
What I Read: Minerva, Quantitative Reasoning
2022-08-03
What I Read: State of Data Engineering 2022
2022-08-02
What I Read: Neural-Implicit Representations, 3D Shapes
2022-08-01
What I Read: Annotated Diffusion Model
2022-07-27
What I Read: Against Naive AI Scaling
2022-07-26
What I Read: Text Embeddings Visually Explained
2022-07-25
What I Read: data replication in production
2022-07-20
What I Read: What is Reinforcement Learning
2022-07-18
What I Read: Death of Data Modeling
2022-07-14
What I Read: Exploring Virtual Worlds, AI
2022-07-13
What I Read: Make the Universe Think
2022-07-11
What I Read: Weak Supervision
2022-07-06
What I Read: Dynamic Time Warping
2022-07-05
What I Read: Bundling into the Database
2022-06-28
What I Read: Deploying Deep Learning
2022-06-27
What I Read: Applying BERT to Speech
2022-06-14
What I Read: Should Warehouse Be Immutable?
2022-06-13
What I Read: Modern Stack for ML Infrastructure
2022-06-08
What I Read: Bandits for Recommender Systems
2022-06-07
What I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data
2022-06-06
What I Read: Beyond Message Passing, Graph Neural Networks
2022-06-01
What I Read: Learning, not Enough Data Part 3
2022-05-31
What I Read: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
2022-05-30
What I Read: Supervised Contrastive Learning
2022-05-25
What I Read: Real World Recommendation System
2022-05-24
What I Read: Understanding, Simple AI
2022-05-23
What I Read: Dataset-Centric Visualization
2022-05-18
What I Read: forecasting, quantile functions
2022-05-17
What I Read: data, distributions
2022-05-16
What I Read: Graph ML, missing node features
2022-05-11
What I Read: Policy Regulariser, Adversary
2022-05-10
What I Read: Generalization of SGD
2022-05-09
What I Read: Machine Learning, Building Blocks of Computing
2022-05-04
What I Read: Deep Learning From First Principles
2022-05-03
What I Read: Taxonomy of Tech Debt
2022-05-02
What I Read: Brain-Inspired Hardware
2022-04-27
What I Read: Bootstrapping Labels
2022-04-26
What I Read: Will Transformers Take Over Artificial Intelligence?
2022-04-25
What I Read: Data Observability vs. Data Testing
2022-04-20
What I Read: Expressiveness in Visualization
2022-04-19
What I Read: never speak of these values
2022-04-18
What I Read: One Voice Detector to Rule Them All
2022-04-13
What I Read: Textless NLP
2022-04-12
What I Read: Economics of Data Businesses
2022-04-11
What I Read: Why Bigger Neural Networks Do Better
2022-04-06
What I Read: Data Distribution Shifts
2022-04-05
What I Read: Musings on typicality
2022-04-04
What I Read: Scale Real-time Data Infrastructure
2022-03-30
What I Read: Researchers Build AI That Builds AI
2022-03-29
What I Read: Statistical Critiques That Don’t Quite Work
2022-03-28
What I Read: Experiment without the wait
2022-03-23
What I Read: Visual Explanation of Classifiers
2022-03-22
What I Read: Principles that Boost Innovation
2022-03-21
What I Read: ML model drift in production
2022-03-17
What I Read: Aristotle, Deep Learning
2022-03-16
What I Watch: Engineering For Data
2022-03-15
What I Read: marketplace ranking infrastructure
2022-03-14
What I Read: evaluate online machine learning models
2022-03-09
What I Read: Real-time machine learning
2022-03-08
What I Read: Engineering Trade-Offs in Automatic Differentiation
2022-03-07
What I Read: Generative Modeling by Estimating Gradients
2022-03-02
What I Read: AI Accelerators, Very Rich Landscape
2022-03-01
What I Read: AI Accelerators, Architectural Foundations
2022-02-28
What I Read: AI Accelerators
2022-02-23
What I Read: How Should Organizations Structure their Data?
2022-02-22
What I Read: MCMC for big datasets
2022-02-21
What I Read: Bayesian Geometry
2022-02-16
What I Read: To Understand Language is to Understand Generalization
2022-02-15
What I Read: Ways I Use Testing as a Data Scientist
2022-02-14
What I Read: Interpretable Time Series
2022-02-09
What I Read: Dataset Distillation
2022-02-08
What I Read: How to Train Decision-Making AIs
2022-02-07
What I Read: What Does It Mean for AI to Understand?
2022-02-02
What I Read: MLOps Documentation
2022-02-01
What I Read: AI Researchers Fight Noise by Turning to Biology
2022-01-31
What I Read: Semi-Supervised Learning
2022-01-26
What I Read: Einstein Summation in Deep Learning
2022-01-25
What I Read: How Kalman filter works
2022-01-24
What I Read: Cloud Wars, Attack of Snowflakes
2022-01-19
What I Read: Permutation Tests
2022-01-18
What I Learn: Meta-Learning, Keyphrase Extraction
2022-01-17
What I Read: Gaussian Process, Active Learning in Physics
2022-01-12
What I Read: Exploring beauty of pure mathematics
2022-01-11
What I Read: how cloud will be reshuffled
2022-01-10
What I Read: Neural-Control Family
2022-01-05
What I Read: Maps of Model Space, Stan
2022-01-04
What I Read: AntiPatterns, MLOps
2022-01-03
What I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology
2021-12-29
What I Read: Model Ensembles Are Faster
2021-12-28
What I Read: Brains Predict Their Perceptions
2021-12-27
What I Read: Improving a Machine Learning System (Part 2 - Features)
2021-12-22
What I Read: Improving a Machine Learning System (Part 1 - Broken Abstractions)
2021-12-21
What I Read: From Data Engineer to SysAdmin: Put down the K8s cluster
2021-12-20
What I Read: Lessons on ML Platforms
2021-12-15
What I Read: Deep Learning Optimization Theory
2021-12-14
What I Read: Dense Vectors
2021-12-13
What I Read: Non-Technical Guide to Interpreting SHAP
2021-12-08
What I Read: True Stories of Algorithmic Improvement
2021-12-07
What I Read: what is a Gaussian process?
2021-12-06
What I Read: Her Machine Learning Tools Pull Insights From Cell Images
2021-12-01
What I Read: Autonomous Building of Composable Models
2021-12-01
What I Read: Limits Discovered in Quest for Optimal Solutions
2021-11-30
What I Read: How to assign partial credit on an exam of true-false questions
2021-11-29
What I Read: machine learning with differential privacy
2021-11-23
What I Read: A Behind-the-Scenes Look at How Postman’s Data Team Works
2021-11-22
What I Read: Is the data engineer still the “worst seat at the table?”
2021-11-18
What I Read: Machine learning is just statistics + quantifier reversal
2021-11-17
What I Read: First-Principles Theory of Neural Network Generalization
2021-11-16
What I Read: Deciding Which Tasks Should Train Together
2021-11-15
What I Read: Introduction AutoEncoder
2021-11-10
What I Read: Data Orchestration w/ Nick Schrock (Elementl)
2021-11-09
What I Read: The Brain Processes Speech in Parallel With Other Sounds
2021-11-08
What I Read: How Train Large Deep Learning Models
2021-11-03
What I Read: ETL Pipelines with Airflow
2021-11-02
What I Read: The Uselessness of Useful Knowledge
2021-11-01
What I Read: Neuron Bursts Can Mimic Famous AI Learning Strategy
2021-10-27
What I Watch: Streaming Data Systems w/ Eric Sammer (Decodable)
2021-10-26
What I Read: How to Train Really Large Models
2021-10-25
What I Read: binary cross-entropy, log loss
2021-10-20
What I Read: Machine learning is not nonparametric statistics
2021-10-19
What I Read: How Animals Map 3D Spaces
2021-10-18
What I Read: Wavelets Transform Data
2021-10-13
What I Read: Mystery of Deep Learning
2021-10-12
What I Learn: Scaling TensorFlow
2021-10-11
What I Learn: Robots Must Be Ephemeralized
2021-10-07
What I Read: Bayesian Media Mix Modeling
2021-10-06
What I Read: learning-to-rank
2021-10-05
What I Read: Permutation-Invariant Neural Networks for Reinforcement Learning
2021-10-04
What I Read: Deep One-class Classification
2021-09-30
What I Read: introduction to machine learning compilers and optimizers
2021-09-29
What I Read: Understanding Convolutions on Graphs
2021-09-28
What I Read: Introduction to Graph Neural Networks
2021-09-27
What I Read: How Computationally Complex Is a Neuron?
2021-09-23
What I Read: How Generally Capable Agents Trained
2021-09-22
What I Read: AI Story Generation
2021-09-21
What I Read: Dissecting “Noise”
2021-09-20
What I Read: Learning Neural Network Subspaces
2021-09-18
What I Read: To Learn, Brain Cells Break DNA
2021-09-16
What I Read: Graph Theory Into New Dimensions
2021-09-15
What I Read: Machines Can Learn, Can They Unlearn?
2021-09-14
What I Read: Systems for Machine Learning
2021-09-13
What I Read: XGBoost, Order Does Matter
2021-09-11
What I Read: The Brain Doesn’t Think the Way You Think It Does
2021-09-09
What I Read: The dysfunctions of Data Engineering
2021-09-08
What I Read: Machine Learning, Rendezvous Architecture
2021-09-07
What I Read: Computer Scientists Discover Limits of Major Research Algorithm
2021-09-06
What I Read: Machine Learning Wont Solve Natural Language Understanding
2021-09-02
What I Read: Pathfinder, A parallel quasi-Newton algorithm
2021-09-01
What I Read: Against SQL
2021-08-31
What I Read: Advances in TF-Ranking
2021-08-30
What I Read: demystifying graph deep learning
2021-08-26
What I Read: The one data platform to rule them all
2021-08-25
What I Read: Multi-task Prediction of Organ Dysfunction
2021-08-24
What I Read: Not Optimized By Jax, PyTorch, or Tensorflow
2021-08-23
What I Read: Why Deep Learning Works
2021-08-19
What I Read: AI-Generating Algorithms, Evolutionary RL
2021-08-18
What I Read: Diffusion Models
2021-08-17
What I Read: I Like Notebooks
2021-08-16
What I Read: Geometric, Deep Learning
2021-08-13
What I Read: Training AI, Analogies
2021-08-12
What I Read: Building Data Platform
2021-08-11
What I Read: Identifying Document Types
2021-08-10
What I Read: Data Science Peer Review
2021-08-09
What I Read: Prompting, Language Models, NLP
2021-08-06
What I Read: Abstraction, Data Science
2021-08-05
What I Read: Understanding Levenshtein Distance
2021-08-04
What I Read: Neurons, Encode, Timing, Firing
2021-08-03
What I Read: Gradient Pseudo-Swap
2021-08-02
What I Read: SwAV method
2021-07-30
What I Read: CNN Heat Maps, Class Activation Mapping
2021-07-29
What I Read: Representation quality, complexity
2021-07-28
What I Read: Parallelizing neural networks, GPU, JAX
2021-07-27
What I Read: Better computer vision models, Transformers, CNNs
2021-07-26
What I Read: Model Free
2021-07-23
What I Read: Causal Inference, Elasticity Pricing
2021-07-22
What I Read: Languages, number of terms for colors
2021-07-21
What I Read: Double Machine Learning for causal inference
2021-07-20
What I Read: Semantic Search
2021-07-19
What I Read: Same or Different? The Question Flummoxes Neural Networks
2021-07-15
What I Read: Do Multi-Task Learning Intelligently
2021-07-14
What I Read: Tabular Data, Deep Learning is Not All You Need
2021-07-13
What I Read: How troubleshoot memory problems in Python
2021-07-12
What I Read: Dask vs Vaex
2021-07-08
What I Read: null hypothesis significance testing falls apart when considering replications
2021-07-07
What I Read: Five types of thinking
2021-07-06
What I Read: What is a Data Mesh?
2021-07-05
What I Read: Data Scientists Need to Know How to Code
2021-07-01
What I Read: Contrastive Representation Learning
2021-06-30
What I Read: Basis and Change of Basis
2021-06-29
What I Read: Comprehensive Guide to Ensemble Learning
2021-06-28
What I Read: Can Model Monitor Another Model?
2021-06-24
What I Read: Human-Centered Explainable AI
2021-06-23
What I Read: What I’ve learned about MLOps
2021-06-22
What I Read: Machine Learning Deserves Better
2021-06-21
What I Read: Hiring Data Scientists
2021-06-17
What I Read: Make Python Code Fast
2021-06-16
What I Read: Knowledge Graphs with Language Model
2021-06-15
What I Read: Dataset Curation for NLP Projects
2021-06-14
What I Read: Productizing Machine Learning Models
2021-06-10
What I Read: Why break rules in data viz
2021-06-09
What I Read: Be Careful Interpreting Predictive Models, Causal Insights
2021-06-08
What I Read: Brain Implant Translates Thoughts of Writing Into Text
2021-06-07
What I Read: Game theory for large-scale data analysis
2021-06-04
What I Read: What is a Vector Database?
2021-06-03
What I Read: Accelerating ML within CNN
2021-06-02
What I Watch: Avoid These Common Mistakes Junior Developers Make
2021-06-01
What I Watch: Continuous Integration vs Feature Branch Workflow
2021-05-31
What I Read: Feature stores
2021-05-28
What I Read: Do Wide and Deep Networks Learn the Same Things?
2021-05-27
What I Read: Neural Nets Solve World’s Hardest Equations Faster
2021-05-26
What I Read: Conda Tips
2021-05-25
What I Read: RL, Decentralized Multi-agent Navigation
2021-05-24
What I Read: Parallel Bayesian Optimization
2021-05-21
What I Read: Bayesian and frequentist results
2021-05-20
What I Read: Clustergam
2021-05-19
What I Watch: Visualising software architecture, C4 model
2021-05-18
What I Read: Adversarial Neural Cryptography
2021-05-17
What I Watch: When To Use Microservices
2021-05-14
What I Read: AI, Colon Cancer
2021-05-13
What I Read: 3 Statistical Paradoxes
2021-05-12
What I Read: Continuous Training Strategy
2021-05-11
What I Read: Models of Data Science teams
2021-05-10
What I Read: Reducing Toxicity in Language Models
2021-05-07
What I Read: Decentralized AI For Healthcare
2021-05-06
What I Read: Zero-Shot Learning
2021-05-05
What I Read: Weight Banding
2021-05-04
What I Read: Branch Specialization
2021-05-03
What I Read: Visualizing Weights
2021-04-29
What I Read: Understanding Key-Value Databases
2021-04-28
What I Read: Why machine learning struggles with causality
2021-04-27
What I Read: Object-Oriented Programming Disaster
2021-04-26
What I Read: Computer Scientist Who Tackles Inequality
2021-04-25
What I Read: Ezra Klein Interviews Alison Gopnik
2021-04-24
What I Read: Deep Learning Recommendation Models
2021-04-22
What I Read: What did COVID do to models?
2021-04-21
What I Read: Compare ML Experiment Tracking Tools
2021-04-20
What I Read: AutoML, Multi-task learning, Multi-tower models, Ads
2021-04-19
What I Read: Scaling vs. Normalizing Data
2021-04-18
What I Read: Brain ‘Rotates’ Memories to Save Them From New Sensations
2021-04-17
What I Read: Rip van Winkles Razor, Adaptive Data Analysis
2021-04-15
What I Read: Unit testing best practices
2021-04-14
What I Read: Common Errors when Debugging Airflow DAGs
2021-04-13
What I Read: Software Engineering Best Practices for Data Scientists
2021-04-12
What I Read: My Love / Hate Relationship With Jupyter
2021-04-10
What I Read: Deep learning model compression
2021-04-09
What I Read: New Algorithm, Linear Equations
2021-04-06
What I Read: Statistics, Geometry Problem
2021-04-05
What I Watch: Regaining Control in Deep Systems
2021-04-04
What I Read: Exploiting machine learning pickle files
2021-04-03
What I Watch: How to Debug Your Team
2021-04-02
What I Read: Bayesian Hierarchical Modelling at Scale
2021-04-01
What I Read: Causal design patterns
2021-03-31
What I Read: Generalization in Deep Learning
2021-03-30
What I Read: Kedro Pipelines with Airflow
2021-03-29
What I Read: Reducing High Cost of Training NLP Models
2021-03-28
What I Read: 15 common coding mistakes data scientist make
2021-03-27
What I Read: Difficulty of Graph Anonymisation
2021-03-26
What I Read: Language Model Fine-tuning
2021-03-25
What I Read: Python Concurrency
2021-03-24
What I Read: Data Quality Management
2021-03-23
What I Read: Neural Nets, How Brains Learn
2021-03-22
What I Read: Continual Learning, Amnesia, Neural Networks
2021-03-21
What I Read: Deep learning, black box
2021-03-20
What I Watch: Why Distributed Systems Are Hard
2021-03-19
What I Read: Adversarial generation of extreme samples
2021-03-18
What I Read: Where Programming, Ops, AI, and the Cloud are Headed
2021-03-17
What I Read: technical debt, ML pipelines
2021-03-16
What I Read: Ensemble, knowledge distillation, and self-distillation
2021-03-15
What I Read: ML Models are Missing Contracts
2021-03-14
What I Read: MLOps for effective AI strategy
2021-03-13
What I Read: Transformer Networks to Answer Questions About Images
2021-03-12
What I Read: Interpretation for Image Recognition
2021-03-11
What I Read: Neural Text Generation
2021-03-10
What I Read: Why I’m lukewarm on graph neural networks
2021-03-09
What I Read: How Transformers work
2021-03-08
What I Read: Medicines Machine Learning Problem
2021-03-07
What I Read: definitive guide to AI monitoring
2021-03-05
What I Read: Data-efficient image Transformers
2021-03-03
What I Read: Machine learning is going real-time
2021-03-01
What I Watch: Future of Data Engineering
2021-03-01
What I Read: Long Live Data Discovery
2021-02-28
What I Read: NeRF Explosion 2020
2021-02-28
What I Watch: The medical test paradox, Can redesigning Bayes rule help?
2021-02-27
What I Read: Dismal Spring Awaits Unless We Slow Covid-19
2021-02-27
What I Read: XGBoost, What it is
2021-02-26
What I Read: Can you trust AutoML?
2021-02-26
What I Read: MLOps Changing How Machine Learning Models Developed
2021-02-25
What I Read: Feature Store vs Data Warehouse
2021-02-25
What I Read: Simplicity Creates Inequity, Fairness, Stereotypes, and Interpretability
2021-02-24
What I Watch: Modern Machine Learning Platform on Kubernetes
2021-02-24
What I Read: Understanding Unintended Consequences of Machine Learning
2021-02-23
What I Read: Deploying Machine Learning, a Survey of Case Studies
2021-02-23
What I Read: Introduction to Graph Neural Networks
2021-02-22
What I Read: Associative Memories
2021-02-22
What I Read: How Build Production Workflow SQL
2021-02-21
What I Read: Approximate Nearest Neighbor Search in Vespa
2021-02-21
What I Read: What If You Never Get Better From Covid-19?
2021-02-20
What I Read: Interpretability in Machine Learning
2021-02-20
What I Read: What is Data Observability?
2021-02-19
What I Read: Building Robust Machine Learning Systems
2021-02-19
What I Watch: Functional Data Engineering
2021-02-18
What I Read: Building a Gigascale ML Feature Store
2021-02-18
What I Read: HuggingFace Transformers
2021-02-17
What I Read: Cellular Automata in Stream Learning
2021-02-17
What I Read: Covid Survivors, Lingering Health Issues
2021-02-16
What I Read: Dynamic Data Testing
2021-02-16
What I Read: Isolation Forest
2021-02-15
What I Read: Revisiting Sutton’s Bitter Lesson for AI
2021-02-15
What I Read: The way we train AI is flawed
2021-02-14
What I Read: How DAGs grow
2021-02-14
What I Read: long-term symptoms of Covid-19
2021-02-13
What I Read: Mismatches between Optimization Analyses and Deep Learning
2021-02-13
What I Read: US Government Will Pay Doctors to Use AI Algorithms
2021-02-12
What I Read: Why is life expectancy in the US lower?
2021-02-11
What I Read: Causal Reasoning in Probability Trees
2021-02-11
What I Read: Structural Time Series
2021-02-10
What I Read: Covid Survivors Long-Term Symptoms
2021-02-10
What I Read: Essential data science skills
2021-02-09
What I Read: Comparing Data Version Control Tools - 2020
2021-02-09
What I Read: Frameworks Scaling Deep Learning Training
2021-02-08
What I Read: New Study About Color to Decode ‘The Brain’s Pantone’
2021-02-08
What I Read: Switchback Tests and Randomized Experimentation Under Network Effects
2021-02-07
What I Read: Attention with Performers
2021-02-07
What I Read: Reproducing Deep Double Descent
2021-02-06
What I Read: Deep Double Descent: Where Bigger Models and More Data Hurt
2021-02-06
What I Read: Genes Evolving in Genome’s Junkyard
2021-02-05
What I Read: AI for good, think form extraction
2021-02-05
What I Read: Explainable AI, 2-Stage Approach
2021-02-04
What I Read: Architectures for Modern Data Infrastructure
2021-02-04
What I Read: “Less than one”-shot learning
2021-02-03
What I Read: Brain Cell DNA Refolds to Aid Memory
2021-02-03
What I Read: What Color Is This? Part 2
2021-02-02
What I Read: Reinforcement learning is supervised learning
2021-02-02
What I Read: Software Tips for Data Science
2021-02-01
What I Read: AI Diagnose Illnesses if Rich
2021-02-01
What I Read: Neural Networks Help Explain Brains
2021-01-31
What I Read: Can a neural network train other networks?
2021-01-31
What I Read: Transformers for Image Recognition
2021-01-30
What I Read: automatic differentiation with graphs
2021-01-30
What I Read: How balance drug prices and innovation (part 2)
2021-01-29
What I Read: The power of the full-stack data science generalist
2021-01-29
What I Read: The way medical professionals are paid keeps structural racism alive
2021-01-28
What I Read: Production with Deep Semi-Supervised Learning
2021-01-28
What I Read: Sparse Gaussian Processes with Spherical Harmonic Features
2021-01-27
What I Read: AI Can Help Patients If Doctors Understand It
2021-01-27
What I Read: data quality, ML Ops
2021-01-26
What I Read: AI’s limitations
2021-01-26
What I Read: Neural Architecture Search
2021-01-25
What I Read: How balance drug prices and innovation
2021-01-25
What I Read: This AI learns by reading the web
2021-01-24
What I Read: Best Practices for Building Machine Learning at Scale
2021-01-24
What I Read: Can Neural Networks Show Imagination?
2021-01-23
What I Read: Multi-Armed Bandits and Experimentation
2021-01-23
What I Read: Traffic prediction with Graph Neural Networks
2021-01-22
What I Read: Bayesian Product Ranking
2021-01-22
What I Read: Continual Learning... and the Bridge to Active and Open World Learning
2021-01-21
What I Read: Transformer Architecture
2021-01-20
What I Read: Biden’s Covid-19 Plan Is Maddeningly Obvious
2021-01-20
What I Read: testing for machine learning
2021-01-20
What I Read: why Switch from Jupyter Notebook to Scripts
2021-01-19
What I Read: Autotuning Multi-Objective Optimization
2021-01-19
What I Read: Maintaining Machine Learning in Production
2021-01-18
What I Read: Floating-Point and Deep Learning
2021-01-18
What I Read: Improving AI Economics
2021-01-17
What I Read: Air pollution is worse than we thought
2021-01-17
What I Read: Data Scientists Should Be More End-to-End
2021-01-16
What I Read: Open-source bionic leg
2021-01-16
What I Read: Think Like an Epidemiologist
2021-01-15
What I Read: Covid-19, Most of Us Have Risk Exactly Backward
2021-01-15
What I Read: End-to-End Machine Learning Platforms
2021-01-14
What I Read: R squared Does Not Measure Predictive Capacity
2021-01-14
What I Read: ScaNN, Efficient Vector Similarity Search
2021-01-13
What I Read: Effort to Stop the Coronavirus in Nursing Homes
2021-01-13
What I Read: Snorkel Tutorial to Predict Multiple Sclerosis
2021-01-12
What I Read: Intro to Data Engineering for Data Scientists
2021-01-12
What I Read: Mitochondria, Anxiety and Mental Health
2021-01-11
What I Read: Making Netflix’s Data Infrastructure Cost-Effective
2021-01-11
What I Read: Patients aren’t being told about AI systems
2021-01-10
What I Read: A winners curse adjustment
2021-01-10
What I Read: This Algorithm Doesnt Replace Doctors
2021-01-09
What I Read: The Cost of AI Training is Improving
2021-01-09
What I Read: the Effort to Stop Maternal Deaths
2021-01-08
What I Read: Count, data notebook
2021-01-08
What I Read: ML For Python Developers
2021-01-07
What I Read: Mathematician’s Guide to Contagion
2021-01-07
What I Read: Running Machine Learning at Scale
2021-01-06
What I Read: Software engineering for Data Scientists
2021-01-06
What I Read: start deploying
2021-01-05
What I Read: making machine learning actually useful
2021-01-05
What I Read: Progress of Natural Language Processing
2021-01-04
What I Read: Symbolic Models from Deep Learning
2021-01-04
What I Read: Who Is Responsible When Autonomous Systems Fail?
2021-01-03
What I Read: Computers Roll Loaded Dice
2021-01-03
What I Read: GPT-3, a Giant Step for NLP
2021-01-02
What I Read: Five Cognitive Biases
2021-01-02
What I Read: Nitpicking ML Technical Debt
2021-01-01
What I Read: data distributions
2021-01-01
What I Read: Differentiable Reasoning over Text
2020-12-31
What I Read: Flows for simultaneous manifold learning and density estimation
2020-12-31
What I Read: Scientists Taught Mice to Smell an Odor That Doesn’t Exist
2020-12-30
What I Read: Learning Neural Causal Models
2020-12-30
What I Read: To Adapt to Tech, We’re Heading Into the Shadows
2020-12-29
What I Read: Fundamental Theorem for Epidemiology
2020-12-29
What I Read: Penrose, Mathematical Notation to Beautiful Diagrams
2020-12-28
What I Read: GPT-3, The New Mighty Language Model
2020-12-28
What I Read: Self Supervised Representation Learning in NLP
2020-12-27
What I Read: ML models in production
2020-12-27
What I Read: Symbolic Mathematics, Neural Networks
2020-12-26
What I Read: Exploring Bayesian Optimization
2020-12-26
What I Read: Possible Alzheimer’s Treatment
2020-12-25
What I Read: Deep Generative Models
2020-12-25
What I Read: Feature Management
2020-12-24
What I Read: Neural Networks to Find Answers in Tables
2020-12-24
What I Read: regularization of linear models
2020-12-23
What I Read: Common Sense Computers
2020-12-23
What I Read: DevOps for ML Data
2020-12-22
What I Read: Coding habits for data scientists
2020-12-22
What I Read: Tonks Multi-Task Model
2020-12-21
What I Read: Developers Databases
2020-12-21
What I Read: Peer Reviewing Data Science Projects
2020-12-20
What I Read: Guide to Graph Neural Networks
2020-12-20
What I Read: Monitoring Machine Learning Models in Production
2020-12-19
What I Read: Diffusion Map for manifold learning
2020-12-19
What I Read: sub-linear deep learning algorithm
2020-12-18
What I Read: Introduction to Circuits
2020-12-18
What I Watch: Probabilities of probabilities binomial
2020-12-17
What I Read: doctor, one story at a time
2020-12-17
What I Read: Transformers Graph Neural Networks
2020-12-16
What I Read: Does Time Really Flow?
2020-12-16
What I Read: Neural State Machine
2020-12-15
What I Read: forecast suicide rates
2020-12-15
What I Read: Kids’ brains building AI
2020-12-14
What I Read: Strictly-Typed Schemas
2020-12-14
What I Read: The New Business of AI
2020-12-13
What I Read: Mental Shortcuts Physician Errors
2020-12-13
What I Read: Neighbourhood Components Analysis
2020-12-12
What I Read: Reproducible Machine Learning
2020-12-12
What I Read: Tiny Brain Cells Connect Mental and Physical Health
2020-12-11
What I Read: Economics of AI
2020-12-11
What I Read: phones detect health problems
2020-12-10
What I Read: HDBSCAN and Density-Based Clustering
2020-12-10
What I Read: Reformer efficient Transformer
2020-12-09
What I Read: Artificial Intelligence Will Do What We Ask
2020-12-09
What I Read: Chain Pharmacies Patients Risk
2020-12-08
What I Read: America health care cost problem Maryland
2020-12-08
What I Read: Replacing do-calculus with Bayes rule
2020-12-07
What I Read: Bayesian Neural Networks Need Not Concentrate
2020-12-07
What I Read: Help AI See in Higher Dimensions
2020-12-06
What I Read: The Case for Bayesian Deep Learning
2020-12-06
What I Read: Visualizing Geometric Harmonic Means
2020-12-05
What I Read: AI Epidemiologist First Warnings Virus
2020-12-05
What I Read: Cutting Food Stamps Can Add Costs Elsewhere
2020-12-04
What I Read: Bad Medicine, Harm That Comes From Racism
2020-12-04
What I Read: Element-wise Active Information Acquisition
2020-12-03
What I Comment: Artificial Intelligence Makes Bad Medicine Even Worse
2020-12-03
What I Read: scalable graph machine learning
2020-12-02
What I Read: A.I. Is Learning to Read Mammograms
2020-12-02
What I Read: the measure of intelligence
2020-12-01
What I Read: In the U.S., an Angioplasty Costs
2020-12-01
What I Read: Reproducibility of Machine Learning in Health Care
2020-11-30
What I Read: Hidden Computational Power in Neurons
2020-11-30
What I Read: These Patients Are Hard to Treat
2020-11-29
What I Read: A.I. Comes to the Operating Room
2020-11-29
What I Read: The Ultimate Guide to Model Retraining
2020-11-28
What I Watch: Bayes theorem
2020-11-28
What I Read: Medical device surveillance with electronic health records
2020-11-28
What I Read: The Watch Is Smart
