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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.
Explains how T5 uses span corruption for pre-training. Topics include sentinel tokens, geometric span sampling, the C4 corpus.
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.
Control AI agent actions with least-privilege permissions, confirmation steps, and sandboxing. Covers tool allowlists, sensitive operations, and safe defaults.
QLoRA combines 4-bit quantization with low-rank adapters to fine-tune large language models on limited hardware while preserving model quality.
Implement content safety and moderation in AI agents, including system-level instructions, output filtering, pattern blocking, graceful refusals.
Covers SARIMA (Seasonal AutoRegressive Integrated Moving Average) for forecasting time series with seasonal patterns.
Whisper applies encoder-decoder transformers to multilingual speech recognition, translation, and language identification across noisy audio conditions.
Use observability for continuous agent improvement. Topics include patterns in logs, turn observations into targeted improvements, track quantitative metrics.
Covers exponential smoothing for time series forecasting, including simple, double (Holt's), and triple (Holt-Winters) methods.
Flamingo connects frozen vision and language models through gated cross-attention, supporting few-shot image and video tasks without task-specific fine-tuning.
Read agent logs, trace reasoning chains, identify common problems, and systematically debug AI agents.
Covers LLaMA's architectural choices including RMSNorm, SwiGLU, and RoPE, plus training data strategies.
Covers Google's PaLM, the 540 billion parameter language model that demonstrated advance capabilities in complex reasoning, multilingual understanding.
Add logging to AI agents to debug behavior, track decisions, and monitor tool usage. Includes practical Python examples.
Examines Qwen's architectural innovations including GQA, SwiGLU activation, and multilingual tokenization.
Explains how the unigram LM tokenizer trains via the EM algorithm, uses Viterbi decoding for segmentation, and enables subword regularization.
Covers Prophet time series forecasting including additive decomposition, trend modeling, seasonal patterns, and holiday effects.
Examines Mistral 7B's architectural innovations including sliding window attention, grouped query attention, and rolling buffer KV cache.
Covers HELM (Holistic Evaluation of Language Models), the early evaluation framework.
Create feedback loops that continuously improve your AI agent through real-world usage data, pattern analysis, and targeted improvements.
Covers GQA, the attention mechanism behind LLaMA 2 and Mistral. Topics include KV head sharing, memory savings, implementation, and quality tradeoffs.
Explains how Multi-Query Attention reduces KV cache memory by sharing keys and values across attention heads, enabling efficient long-context inference.
Explains why traditional word-level approaches fail with diverse text, from OOV words to morphological complexity.
Covers K-means clustering from mathematical foundations to practical implementation. Topics include the algorithm, initialization strategies.
Covers multi-vector retrieval systems introduced in 2021. Explains how token-level contextualized embeddings enabled fine-grained matching.
Create and use test cases to evaluate AI agent performance. Build broad test suites, track results over time.
Examines Microsoft's Phi model family and how textbook-quality training data enables small models to match larger competitors.
Explains how WordPiece differs from BPE by using a likelihood objective for merges. Topics include the PMI score, greedy tokenization, ## prefix notation.
Examines LLaMA's core architectural components: pre-norm with RMSNorm for stable training, SwiGLU feed-forward networks for expressive computation.
Chain-of-thought prompting elicits intermediate reasoning steps from language models. Covers zero-shot and few-shot methods, benefits, and limits.
Define clear, measurable success criteria for AI agents including correctness, reliability, efficiency, safety.
Repetition, frequency, and presence penalties alter token probabilities to reduce loops. Covers n-gram blocking, tuning, and effects on generated text.
Covers t-SNE (t-Distributed Stochastic Neighbor Embedding), including mathematical foundations, probability distributions, KL divergence optimization.
Constrained decoding uses token masks and grammars to produce valid JSON, SQL, and regular-expression matches from language models.
Covers the 2021 Foundation Models Report published by Stanford's CRFM. Explains how this influential report formally defined foundation models.
Examines the trade-offs of multi-agent AI systems, from specialization and parallel processing to coordination challenges and complexity management.
Covers mechanics of autoregressive generation in transformers, including the generation loop, KV caching for efficiency, stopping criteria.
Explains how nucleus sampling dynamically selects tokens based on cumulative probability, solving top-k limitations for coherent and creative text generation.
Covers LIME (Local Interpretable Model-Agnostic Explanations), including mathematical foundations, implementation strategies.
Covers Mixture of Experts (MoE) architectures, including routing mechanisms, load balancing, emergent specialization.
Explains how AI agents exchange information and coordinate actions through structured messages, communication patterns like pub-sub and request-response.
Top-k sampling limits each generation step to the k most probable tokens. Covers probability renormalization, diversity, failure modes, and tuning.
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