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
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
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
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
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
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
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
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
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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MLA 017 AWS Local Development Environment
Machine Learning Guide
AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly within AWS environments, using tools such as Lambda, Cloud9, SageMaker Studio, client VPN connections, or LocalStack, developers can streamline transitions to production and leverage AWS-managed services from the start. This episode outlines three primary strategies for treating AWS as your development environment, details the benefits and tradeoffs of each, and explains the role of infrastructure-as-code tools such as...
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Try a walking desk to stay healthy while you study or work!
Reinforcement Learning (RL) is a fundamental component of artificial intelligence, different from purely being AI itself. It is considered a key aspect of AI due to its ability to learn through interactions with the environment using a system of rewards and punishments.
Links:
Concepts and Definitions
- Reinforcement Learning (RL):
- RL is a framework where an "agent" learns by interacting with its environment and receiving feedback in the form of rewards or punishments.
- It is part of the broader machine learning category, which includes supervised and unsupervised learning.
- Unlike supervised learning, where a model learns from labeled data, RL focuses on decision-making and goal achievement.
Comparison with Other Learning Types
- Supervised Learning:
- Involves a teacher-student paradigm where models are trained on labeled data.
- Common in applications like image recognition and language processing.
- Unsupervised Learning:
- Not commonly used in practical applications according to the experience shared in the episode.
- Reinforcement Learning vs. Supervised Learning:
- RL allows agents to learn independently through interaction, unlike supervised learning where training occurs with labeled data.
Applications of Reinforcement Learning
- Games and Simulations:
- Deep reinforcement learning is used in games like Go (AlphaGo) and video games, where the environment and possible rewards or penalties are predefined.
- Robotics and Autonomous Systems:
- Examples include robotics (e.g., Boston Dynamics mules) and autonomous vehicles that learn to navigate and make decisions in real-world environments.
- Finance and Trading:
- Utilized for modeling trading strategies that aim to optimize financial returns over time, although breakthrough performance in trading isn’t yet evidenced.
RL Frameworks and Environments
- Framework Examples:
- OpenAI Baselines, TensorForce, and Intel's Coach, each with different capabilities and company backing for development.
- Environments:
- OpenAI's Gym is a suite of environments used for training RL agents.
Future Aspects and Developments
- Model-based vs. Model-free RL:
- Model-based RL involves planning and knowledge of the world dynamics, while model-free is about reaction and immediate responses.
- Remaining Challenges:
- Current hurdles in AI include reasoning, knowledge representation, and memory, where efforts are ongoing in institutions like Google DeepMind for further advancement.