Tutorials on Machine Learning

Learn about Machine Learning from fellow newline community members!

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  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
NEW

Retrieval-Augmented Generation for Multi-Turn Prompts

Explore how Retrieval-Augmented Generation enhances multi-turn conversations by integrating real-time data for accurate and personalized responses.
NEW

Hyperparameter Tuning in Hugging Face Pipelines

Master hyperparameter tuning in Hugging Face pipelines to enhance model performance effectively through automated techniques and best practices.

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NEW

Key Metrics for Multimodal Benchmarking Frameworks

Explore essential metrics for evaluating multimodal AI systems, focusing on performance, efficiency, stability, and fairness to ensure reliable outcomes.
NEW

Event-Driven Pipelines for AI Agents

Explore how event-driven pipelines enhance AI agents with real-time processing, scalability, and efficient data handling for modern applications.
NEW

Relative vs. Absolute Positional Embedding in Decoders

Explore the differences between absolute and relative positional embeddings in transformers, highlighting their strengths, limitations, and ideal use cases.

Annotated Transformer: LayerNorm Explained

Explore how LayerNorm stabilizes transformer training, enhances gradient flow, and improves performance in NLP tasks through effective normalization techniques.

How to Scale Hugging Face Pipelines for Large Datasets

Learn practical strategies to efficiently scale Hugging Face pipelines for large datasets, optimizing memory, performance, and workflows.

QLoRA: Fine-Tuning Quantized LLMs

QLoRA revolutionizes fine-tuning of large language models, slashing memory usage and training times while maintaining performance.

How to Choose Embedding Models for LLMs

Choosing the right embedding model is crucial for AI applications, impacting accuracy, efficiency, and scalability. Explore key criteria and model types.

Sequential User Behavior Modeling with Transformers

Explore how transformer models enhance sequential user behavior prediction, offering improved accuracy, scalability, and applications across industries.

Top Tools for LLM Error Analysis

Explore essential tools and techniques for analyzing errors in large language models, enhancing their performance and reliability.

Optimizing Contextual Understanding in Support LLMs

Learn how to enhance customer support with LLMs through contextual understanding and optimization techniques for better accuracy and efficiency.

Real-Time Monitoring for RAG Agents: Key Metrics

Explore essential metrics and challenges in real-time monitoring of Retrieval-Augmented Generation agents to ensure optimal performance and reliability.

How to Evaluate Prompts for Specific Tasks

Learn effective strategies for evaluating AI prompts tailored to specific tasks, ensuring improved accuracy and relevance in outputs.

How to Use Optuna for LLM Fine-Tuning

Learn how to efficiently fine-tune large language models using Optuna's advanced hyperparameter optimization techniques.

Lightweight Transformers with Knowledge Distillation

Explore how lightweight transformers and knowledge distillation enhance AI performance on edge devices, achieving efficiency without sacrificing accuracy.

How RAG Enables Real-Time Knowledge Updates

Explore how Retrieval-Augmented Generation (RAG) enhances real-time knowledge updates, improving accuracy and efficiency across various industries.

How to Debug Bias in Deployed Language Models

Learn how to identify and reduce bias in language models to ensure fair and accurate outputs across various demographics and industries.

Research on Mixed-Precision Training for LLMs

Explore how mixed-precision training revolutionizes large language models by enhancing speed and efficiency while maintaining accuracy.

Best Practices for Evaluating Fine-Tuned LLMs

Learn best practices for evaluating fine-tuned language models, including setting clear goals, choosing the right metrics, and avoiding common pitfalls.

Agentic RAG: Optimizing Knowledge Personalization

Explore the evolution from Standard RAG to Agentic RAG, highlighting advancements in knowledge personalization and AI's role in complex problem-solving.

Error Tracking for LLMs in Cloud Hosting

Learn how effective error tracking for large language models in cloud environments boosts performance, reduces costs, and ensures reliability.

Best Practices for LLM Latency Benchmarking

Optimize LLM latency by mastering benchmarking techniques, key metrics, and best practices for improved user experience and performance.

Energy-Saving Techniques for LLM Inference

Explore effective strategies to reduce energy consumption during large language model inference without sacrificing performance.

Best Practices for Labeling Error Detection

Learn best practices for detecting labeling errors in AI data, combining automated tools and manual reviews for reliable outcomes.

Guide to AI Agent Performance Metrics

Explore vital performance metrics for AI agents, including accuracy, efficiency, and advanced metrics to optimize effectiveness and user satisfaction.

Root Cause Analysis for AI Automation Errors

Explore how Root Cause Analysis can resolve underlying issues in AI automation, improving reliability and reducing costly errors.

Ultimate Guide to Feedback-Driven LLM Fine-Tuning

Explore how feedback-driven fine-tuning enhances the effectiveness of Large Language Models in customer service by leveraging real user insights.

Fine-Tuning Decoder-Only Transformers with LoRA

Learn how LoRA simplifies the fine-tuning of large language models, reducing resource requirements while maintaining performance.

Dynamic Context Injection with Retrieval Augmented Generation

Learn how dynamic context injection and Retrieval-Augmented Generation enhance large language models' performance and accuracy with real-time data integration.