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Covers instruction tuning introduced in 2021. Explains how fine-tuning on diverse instruction-response pairs changed language models.
Covers Isolation Forest covering unsupervised anomaly detection, path length calculations, harmonic numbers, anomaly scoring.
Build a complete AI agent memory system combining conversation history and persistent knowledge storage.
Explains how Position Interpolation extends transformer context windows by scaling position indices to stay within training distributions.
Covers boosted trees and gradient boosting, covering ensemble learning, loss functions, sequential error correction, and scikit-learn implementation.
Covers OpenAI's DALL·E 2, the major text-to-image generation model that combined CLIP-guided diffusion with high-quality image synthesis.
Explains how AI agents store and retrieve information across sessions using vector databases, embeddings, and semantic search.
Examines the fundamental barriers limiting transformer context windows, including quadratic memory scaling, position encoding extrapolation failures.
Codex adapted GPT-3 for code completion and generation. Covers its training data, natural-language prompts, benchmark results, and role in developer tools.
Covers Random Forest covering ensemble learning, bootstrap sampling, random feature selection, bias-variance tradeoff, and implementation in scikit-learn.
Covers OpenAI's DALL·E, the early text-to-image generation model that extended transformer architectures to multimodal tasks.
Build a working calculator tool for your AI agent from scratch. Topics include the complete workflow from Python function to tool integration.
Covers CART (Classification and Regression Trees), including mathematical foundations, Gini impurity, variance reduction.
GPT-3 showed that scaling autoregressive transformers could enable in-context and few-shot learning. Covers its architecture, data, prompting, and limitations.
Call language models from Python code, including GPT-5, Claude Sonnet 4.5, and Gemini 2.5. Covers API integration, error handling.
Covers Google's T5 (Text-to-Text Transfer Transformer) introduced in 2019. Explains how the text-to-text framework unified diverse NLP tasks.
Design effective tool interfaces for AI agents, from basic function definitions to multi-tool orchestration.
Covers logistic regression covering mathematical foundations, the logistic function, optimization algorithms, and practical implementation.
Explains how Longformer combines sliding window and global attention to process documents of 4,096+ tokens with O(n) complexity instead of O(n²).
Implement sparse attention patterns including local windows, strided attention, and block-sparse methods.
Covers Poisson regression for count data analysis. Topics include mathematical foundations, maximum likelihood estimation, rate ratio interpretation.
Transformer-XL extends context with segment recurrence and relative positions. Covers its architecture, efficiency, training method, and influence.
Spline regression models nonlinear relationships with piecewise polynomials. Covers B-splines, knot selection, natural cubic splines, and Python.
Covers multinomial logistic regression covering mathematical foundations, softmax function, coefficient estimation.
Covers OpenAI's GPT-1 and GPT-2 models. Explains how autoregressive pretraining with transformers enabled transfer learning across NLP tasks.
Covers BERT (Bidirectional Encoder Representations from Transformers), including masked language modeling, bidirectional context understanding.
Covers advanced prompting strategies for AI agents including role assignment, few-shot prompting with examples, and iterative refinement.
Covers Elastic Net regularization, including mathematical foundations, geometric interpretation.
Explains how XLNet, RoBERTa, and ALBERT refined BERT through permutation language modeling, optimized training procedures, and architectural efficiency.
Explains how layer normalization enables stable transformer training by normalizing.
Model nonlinear relationships with polynomial regression. Covers feature construction, Python implementation, model selection, bias, variance, and overfitting.
Covers preference-based learning, the framework developed by Christiano et al. in 2017.
Covers what you'll build throughout this book: a capable AI agent that remembers conversations, uses tools, plans tasks, and grows smarter with each chapter.
Covers Ridge regression and L2 regularization, including mathematical foundations, geometric interpretation, bias-variance tradeoff.
Covers FastText and subword tokenization, including character n-gram embeddings, handling out-of-vocabulary words, morphological processing.
Covers residual connections, the architectural innovation that solved the vanishing gradient problem in deep networks.
Covers Google's transition to neural machine translation in 2016. Explains how GNMT replaced statistical phrase-based methods with end-to-end neural networks.
AI agents combine a language model with tools, memory, and control logic. This article separates agents from workflows and multi-agent systems.
Covers GloVe (Global Vectors) and the Adam optimizer, two early 2014 developments that changed neural language processing.
Covers scaled dot-product attention: how queries and keys compute similarity, why dividing by sqrt(d_k) prevents softmax saturation.
Model Context Protocol (MCP) gives LLM applications a standard way to list tools, call them, and exchange structured results across services.
Explains how beam search balances quality and compute in sequence generation, covering greedy decoding limits, length normalization, diverse beam search.
Teacher forcing trains sequence-to-sequence models with ground-truth context. Covers exposure bias, scheduled sampling, and alternatives based on REINFORCE.
Explains how bidirectional LSTMs and GRUs provide full-sequence context by processing text forward and backward, enabling strong sequence labeling.
Covers neural information retrieval, the advance approach that learned semantic representations for queries and documents.
Temperature zero does not make an LLM deterministic. GPU kernels, batching, floating-point arithmetic, and serving choices can still change its output.
Examines Luong attention variants including dot product, general, and concat scoring. Topics include global vs local attention, input-feeding.
Covers layer normalization, the normalization technique that computes statistics across features for each example.
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