702 items · Page 8 of 15
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Covers equity market fundamentals including stock ownership, order book mechanics, trading execution, and key valuation metrics for quantitative finance.
Bahdanau attention replaced fixed context vectors with dynamic source alignment, improving neural machine translation and shaping later transformer models.
Covers root-finding, interpolation, and numerical integration for finance. Compute implied volatility, build yield curves, and price derivatives.
Covers vectors, matrices, and decompositions for portfolio optimization, risk analysis, and factor models. Essential math foundations for quant finance.
Covers moments of returns, hypothesis testing, and confidence intervals. Essential statistical techniques for analyzing financial data and quantifying risk.
Explains how DeepMind's Chinchilla scaling laws changed LLM training by proving models should use 20 tokens per parameter for compute-optimal performance.
Explains how power laws govern neural network scaling. Topics include log-log analysis, fitting techniques, and how to predict model performance at any scale.
Covers probability distributions essential for quantitative finance: normal, lognormal, binomial, Poisson, and fat-tailed distributions with Python examples.
Covers BART's denoising pre-training approach including text infilling, token masking, sentence permutation, and how corruption schemes enable generation.
Covers probability distributions, expected values, Bayes' theorem, and risk measures. Essential foundations for portfolio theory and derivatives pricing.
Explains how T5 reformulates all NLP tasks as text-to-text problems. Topics include task prefixes, classification, NER.
Covers compute-optimal LLM training using Chinchilla scaling laws. Topics include the 20:1 token ratio, practical allocation formulas.
Covers hybrid retrieval systems introduced in 2024. Explains how hybrid systems combine sparse retrieval for fast candidate generation.
Covers structured outputs introduced in language models during 2024. Explains how structured outputs enable reliable data extraction.
Covers multimodal integration in 2024, the advance that enabled AI systems to directly process and understand text, images, audio.
Covers advanced parameter-efficient fine-tuning methods introduced in 2024, including AdaLoRA, DoRA, VeRA, and other innovations.
Covers continuous post-training, including parameter-efficient fine-tuning with LoRA, catastrophic forgetting prevention, incremental model updates.
Covers DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the algorithm.
Covers GPT-4o, including unified multimodal architecture, real-time processing, unified tokenization, advanced attention mechanisms, memory mechanisms.
Covers quadratic programming (QP) for portfolio optimization, including the mean-variance framework, efficient frontier construction.
DeepSeek R1 uses reinforcement learning and distilled reasoning models for mathematical and coding tasks. Covers its training design, results, and limitations.
Covers agentic AI systems introduced in 2024. Explains how AI systems evolved from reactive tools to autonomous agents capable of planning.
A depot-return example connects vehicle capacities and service windows to routing constraints, OR-Tools callbacks, and independently checked plans.
AI co-scientist systems generate hypotheses, plan experiments, and synthesize evidence. Covers autonomous research workflows, evaluation, and human oversight.
Covers minimum cost flow optimization for slotting problems, including network flow theory, mathematical formulation.
Covers V-JEPA 2, including vision-based world modeling, joint embedding predictive architecture, visual prediction, embodied AI.
Transform raw text into structured data through tokenization, normalization, and cleaning techniques.
Covers TF-IDF and Bag of Words, including term frequency, inverse document frequency, vectorization, and text classification.
Covers word embeddings covering Word2Vec skip-gram, GloVe matrix factorization, negative sampling.
Examines Mistral AI's Mixtral models and how they demonstrated that sparse mixture-of-experts architectures could be production-ready.
Covers Mixed Integer Linear Programming (MILP) for factory optimization, covering mathematical foundations, constraint modeling, branch-and-bound algorithms.
Covers specialized large language models for low-resource languages, including synthetic data generation, cross-lingual transfer learning.
Covers CP-SAT rostering using Google OR-Tools to solve complex workforce scheduling problems with binary decision variables, coverage constraints.
Covers Constitutional AI, including principle-based alignment, self-critique training, reinforcement learning from AI feedback (RLAIF), scalability advantages.
Reduce AI agent API costs without sacrificing capability. Topics include model selection, caching, batching, prompt optimization.
Examines multimodal large language models that integrated vision and language capabilities, enabling AI systems to process images and text together.
Covers NHITS (Neural Hierarchical Interpolation for Time Series), a deep learning architecture for multi-scale time series forecasting.
Covers the 2023 open LLM wave, including MPT, Falcon, Mistral, and other open models. Explains how these models created a competitive ecosystem.
Covers N-BEATS, an interpretable deep learning architecture for time series forecasting.
LLaMA showed that carefully trained smaller models could match larger systems. Covers model sizes, data choices, efficiency, and open research access.
Covers GPT-4, including multimodal capabilities, improved reasoning abilities, enhanced safety and alignment, human-level performance on standardized tests.
Deploy your AI agent from a local script to a production service. Topics include packaging, cloud deployment, APIs, and making your agent accessible to users.
Covers HDBSCAN clustering algorithm covering density-based clustering, automatic cluster selection, noise detection.
Covers BIG-bench (Beyond the Imitation Game Benchmark) and MMLU (Massive Multitask Language Understanding), the widely used evaluation benchmarks.
Establish ethical guidelines and implement human oversight for AI agents. Topics include defining core principles, encoding ethics in system prompts.
Covers T5's encoder-decoder architecture, relative position biases, span corruption pretraining, and text-to-text framework for unified NLP tasks.
Covers hierarchical clustering, including dendrograms, linkage criteria (single, complete, average, Ward), and scikit-learn implementation.
Covers function calling capabilities in language models from 2023, including structured outputs, tool interaction, API integration.
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I created this space so readers can learn together, ask questions, and make sense of difficult ideas.