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MLG 035 Large Language Models 2

Machine Learning Guide

Release Date: 05/08/2025

MLG 036 Autoencoders show art MLG 036 Autoencoders

Machine Learning Guide

Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at   - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to  from  for recording...

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MLG 035 Large Language Models 2 show art MLG 035 Large Language Models 2

Machine Learning Guide

At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...

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MLG 034 Large Language Models 1 show art MLG 034 Large Language Models 1

Machine Learning Guide

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...

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MLA 024 Code AI MCP Servers, ML Engineering show art MLA 024 Code AI MCP Servers, ML Engineering

Machine Learning Guide

Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at  stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of...

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MLA 023 Code AI Models & Modes show art MLA 023 Code AI Models & Modes

Machine Learning Guide

Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at  stay healthy & sharp while you learn & code Model Current Leaders According to the  (as of April 12, 2025), leading...

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MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf show art MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf

Machine Learning Guide

Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at   stay healthy & sharp while you learn & code Definition and...

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MLG 033 Transformers show art MLG 033 Transformers

Machine Learning Guide

Links: Notes and resources at 3Blue1Brown videos:   stay healthy & sharp while you learn & code  audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped...

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MLA 021 Databricks: Cloud Analytics and MLOps show art MLA 021 Databricks: Cloud Analytics and MLOps

Machine Learning Guide

Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at   stay healthy & sharp while you learn & code Raybeam and Databricks Raybeam is a...

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MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes show art MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes

Machine Learning Guide

Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at   stay healthy & sharp while you learn & code  - Data Scientist...

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MLA 019 Cloud, DevOps & Architecture show art MLA 019 Cloud, DevOps & Architecture

Machine Learning Guide

The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at   stay healthy & sharp while you learn...

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More Episodes

At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction.

Links

In-Context Learning (ICL)

  • Definition: LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters.
    • Types:
      • Zero-shot: Direct query, no examples provided.
      • One-shot: Single example provided.
      • Few-shot: Multiple examples, balancing quantity with context window limitations.
    • Mechanism: ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations.
    • Emergent Properties: ICL is an "inference-time training" approach, leveraging the model’s pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples.

Retrieval Augmented Generation (RAG) and Grounding

  • Grounding: Connecting LLMs with external knowledge bases to supplement or update static training data.
    • Motivation: LLMs’ training data becomes outdated or lacks proprietary/specialized knowledge.
    • Benefit: Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information.
  • RAG Workflow:
    1. Embedding: Documents are converted into vector embeddings (using sentence transformers or representation models).
    2. Storage: Vectors are stored in a vector database (e.g., FAISS, ChromaDB, Qdrant).
    3. Retrieval: When a query is made, relevant chunks are extracted based on similarity, possibly with re-ranking or additional query processing.
    4. Augmentation: Retrieved chunks are added to the prompt to provide up-to-date context for generation.
    5. Generation: The LLM generates responses informed by the augmented context.
    • Advanced RAG: Includes agentic approaches—self-correction, aggregation, or multi-agent contribution to source ingestion, and can integrate external document sources (e.g., web search for real-time info, or custom datasets for private knowledge).

LLM Agents

  • Overview: Agents extend LLMs by providing goal-oriented, iterative problem-solving through interaction, memory, planning, and tool usage.
  • Key Components:
    • Reasoning Engine (LLM Core): Interprets goals, states, and makes decisions.
    • Planning Module: Breaks down complex tasks using strategies such as Chain of Thought or ReAct; can incorporate reflection and adjustment.
    • Memory: Short-term via context window; long-term via persistent storage like RAG-integrated databases or special memory systems.
    • Tools and APIs: Agents select and use external functions—file manipulation, browser control, code execution, database queries, or invoking smaller/fine-tuned models.
  • Capabilities: Support self-evaluation, correction, and multi-step planning; allow integration with other agents (multi-agent systems); face limitations in memory continuity, adaptivity, and controllability.
  • Current Trends: Research and development are shifting toward these agentic paradigms as LLM core scaling saturates.

Multimodal Large Language Models (MLLMs)

  • Definition: Models capable of ingesting and generating across different modalities (text, image, audio, video).
  • Architecture:
    • Modality-Specific Encoders: Convert raw modalities (text, image, audio) into numeric embeddings (e.g., vision transformers for images).
    • Fusion/Alignment Layer: Embeddings from different modalities are projected into a shared space, often via cross-attention or concatenation, allowing the model to jointly reason about their content.
    • Unified Transformer Backbone: Processes fused embeddings to allow cross-modal reasoning and generates outputs in the required format.
  • Recent Advances: Unified architectures (e.g., GPT-4o) use a single model for all modalities rather than switching between separate sub-models.
  • Functionality: Enables actions such as image analysis via text prompts, visual Q&A, and integrated speech recognition/generation.

Advanced LLM Architectures and Training Directions

  • Predictive Abstract Representation: Incorporating latent concept prediction alongside token prediction (e.g., via autoencoders).
  • Patch-Level Training: Predicting larger “patches” of tokens to reduce sequence lengths and computation.
  • Concept-Centric Modeling: Moving from next-token prediction to predicting sequences of semantic concepts (e.g., Meta’s Large Concept Model).
  • Multi-Token Prediction: Training models to predict multiple future tokens for broader context capture.

Evaluation Benchmarks (as of 2025)

  • Key Benchmarks Used for LLM Evaluation:
    • GPQA (Diamond): Graduate-level STEM reasoning.
    • SWE Bench Verified: Real-world software engineering, verifying agentic code abilities.
    • MMMU: Multimodal, college-level cross-disciplinary reasoning.
    • HumanEval: Python coding correctness.
    • HLE (Human’s Last Exam): Extremely challenging, multimodal knowledge assessment.
    • LiveCodeBench: Coding with contamination-free, up-to-date problems.
    • MLPerf Inference v5.0 Long Context: Throughput/latency for processing long contexts.
    • MultiChallenge Conversational AI: Multiturn dialogue, in-context reasoning.
    • TAUBench/PFCL: Tool utilization in agentic tasks.
    • TruthfulnessQA: Measures tendency toward factual accuracy/robustness against misinformation.

Prompt Engineering: High-Impact Techniques

  • Foundational Approaches:
    • Few-Shot Prompting: Provide pairs of inputs and desired outputs to steer the LLM.
    • Chain of Thought: Instructing the LLM to think step-by-step, either explicitly or through internal self-reprompting, enhances reasoning and output quality.
    • Clarity and Structure: Use clear, detailed, and structured instructions—task definition, context, constraints, output format, use of delimiters or markdown structuring.
    • Affirmative Directives: Phrase instructions positively (“write a concise summary” instead of “don’t write a long summary”).
    • Iterative Self-Refinement: Prompt the LLM to review and improve its prior response for better completeness, clarity, and factuality.
    • System Prompt/Role Assignment: Assign a persona or role to the LLM for tailored behavior (e.g., “You are an expert Python programmer”).
  • Guideline: Regularly consult official prompting guides from model developers as model capabilities evolve.

Trends and Research Outlook

  • Inference-time compute is increasingly important for pushing the boundaries of LLM task performance.
  • Agentic LLMs and multimodal reasoning represent the primary frontiers for innovation.
  • Prompt engineering and benchmarking remain essential for extracting optimal performance and assessing progress.
  • Models are expected to continue evolving with research into new architectures, memory systems, and integration techniques.