A Guide to Live Inference with a Forecasting Model
Beyond offline training and testing predictions- 25671Murphy ≡ DeepGuide
Graph Data Science for Tabular Data
Graph methods are more general than you may think- 28467Murphy ≡ DeepGuide
The Poisson Bootstrap
Bootstrapping over large datasets- 28902Murphy ≡ DeepGuide
Run LLM inference using Apple Hardware
Unlock Apple GPU Power for LLM Inference with MLX- 28316Murphy ≡ DeepGuide
Benchmarking LLM Inference Backends
Comparing Llama 3 serving performance on vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and TGI- 23067Murphy ≡ DeepGuide
Dynamic Execution
Getting your AI task to distinguish between Hard and Easy problems- 27378Murphy ≡ DeepGuide
Combining Large and Small LLMs to Boost Inference Time and Quality
Implementing Speculative and Contrastive Decoding- 29123Murphy ≡ DeepGuide
How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference
With the recent explosion of interest in large language models (LLMs), they often seem almost magical. But let’s demystify them. I wanted to step back and unpack the fundamentals — breaking down how LLMs are built, trained, and fine-tuned to become the AI- 26977Murphy ≡ DeepGuide
Mastering the Poisson Distribution: Intuition and Foundations
Take a dive into the foundations and exemplifying use cases of the Poisson distribution- 23038Murphy ≡ DeepGuide
We look at an implementation of the HyperLogLog cardinality estimati
Using clustering algorithms such as K-means is one of the most popul
Level up Your Data Game by Mastering These 4 Skills
Learn how to create an object-oriented approach to compare and evalu
When I was a beginner using Kubernetes, my main concern was getting
Tutorial and theory on how to carry out forecasts with moving averag