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

Dynamic Role Assignment in Multi-Agent Systems

Explore the transformative impact of dynamic role assignment in multi-agent systems, enhancing efficiency and adaptability in real-time environments.

Pre-Norm vs Post-Norm: Which to Use?

Explore the differences between Pre-Norm and Post-Norm strategies in transformer models to optimize training stability and performance.

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How to Simulate Large-Scale Multi-Agent Systems

Learn how to effectively simulate large-scale multi-agent systems, from selecting frameworks to optimizing performance for complex environments.

ultimate guide to Speculative decoding

Explore how speculative decoding enhances AI text generation by combining speed and quality through a draft-and-verify model approach.

ultimate guide to PagedAttention

PagedAttention enhances GPU memory management for large language models, improving efficiency, scalability, and cost during inference.

ultimate guide to FlashInfer

Explore how a specialized library enhances the efficiency of large language models with advanced attention mechanisms and resource management.

ultimate guide to FlashAttention

Explore how a memory-efficient algorithm enhances large language models by accelerating processing and reducing resource demands.

AutoRound vs AWQ quantization

Explore the differences between AutoRound and AWQ quantization methods for large language models, focusing on accuracy, speed, and use cases.

GPTQ vs AWQ quantization

Explore the differences between GPTQ and AWQ quantization methods for optimizing large language models, focusing on efficiency and accuracy.

ultimate guide to GPTQ quantization

Explore how GPTQ quantization optimizes large AI models for faster performance and reduced resource usage without sacrificing accuracy.

Real-World LLM Testing: Role of User Feedback

User feedback is essential for improving large language models, bridging the gap between benchmarks and real-world performance.

Telemetry Strategies for Distributed Tracing in AI Agents

Explore telemetry strategies for enhancing distributed tracing in AI agents, addressing unique challenges and solutions for effective monitoring.

Best Practices for Debugging Multi-Agent LLM Systems

Explore effective strategies for debugging complex multi-agent LLM systems, addressing challenges like non-determinism and communication breakdowns.

Ultimate Guide to LoRA for LLM Optimization

Learn how LoRA optimizes large language models by reducing resource demands, speeding up training, and preserving performance through efficient adaptation methods.

Trade-Offs in Sparsity vs. Model Accuracy

Explore the balance between model sparsity and accuracy in AI, examining pruning techniques and their implications for deployment and performance.

Fine-tuning LLMs with Limited Data: Regularization Tips

Explore effective regularization techniques for fine-tuning large language models with limited data, ensuring better generalization and performance.

Real-Time CRM Data Enrichment with LLMs

Explore how real-time CRM data enrichment with LLMs enhances customer insights, streamlines operations, and improves decision-making.

GPU Bottlenecks in LLM Pipelines

Learn how to identify and fix GPU bottlenecks in large language model pipelines for improved performance and scalability.

Fine-Tuning LLMs on a Budget

Learn how to fine-tune large language models effectively on a budget with cost-saving techniques and strategies for optimal results.

Real-Time Debugging for Multi-Agent LLM Pipelines

Explore effective strategies for debugging complex multi-agent LLM systems, enhancing reliability and performance in AI applications.

Fine-Tuning LLMs with Gradient Checkpointing and Partitioning

Explore how gradient checkpointing and model partitioning can optimize memory usage for fine-tuning large language models on limited hardware.

How to Analyze Inference Latency in LLMs

Explore effective strategies to analyze and reduce inference latency in large language models, improving performance and user experience.

Fine-Tuning LLMs with Multimodal Data: Challenges and Solutions

Explore the challenges and solutions of fine-tuning large language models with multimodal data to enhance AI's capabilities across various fields.

Chunking, Embedding, and Vectorization Guide

Learn how chunking, embedding, and vectorization transform raw text into efficient, searchable data for advanced retrieval systems.

On-Prem vs Cloud: LLM Cost Breakdown

Explore the cost implications of on-premise vs. cloud deployment for large language models, focusing on efficiency, scalability, and long-term savings.

Fine-Tuning LLMs for Edge Real-Time Processing

Explore the challenges and strategies for fine-tuning large language models for edge devices to enhance real-time processing, security, and efficiency.

Unit Testing AI Agents: Common Challenges and Solutions

Explore the unique challenges of unit testing AI agents and discover practical solutions to enhance reliability and performance.

Top 5 Benchmarking Frameworks for Scalable Evaluation

Explore five innovative benchmarking frameworks that simplify the evaluation of AI models, focusing on performance, efficiency, and ethical standards.

Memory vs. Computation in LLMs: Key Trade-offs

Explore the trade-offs between memory usage and computational efficiency in deploying large language models to optimize performance and costs.

KV-Cache Streaming for Low-Latency Inference

KV-cache streaming enhances low-latency inference for AI applications, tackling memory usage, network delays, and recomputation costs.