The Complete AI Agent Decision Framework
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Mastering JSON Prompting for LLMs
LLMs
5 Essential Python Scripts for Intermediate Machine Learning Practitioners
As a machine learning engineer, you probably enjoy working on interesting tasks like experimenting with model architectures, fine-tuning hyperparameters, and analyzing results.
Datasets for Training a Language Model
A good language model should learn correct language usage, free of biases and errors.
Building ReAct Agents with LangGraph: A Beginner’s Guide
Expert-Level Feature Engineering: Advanced Techniques for High-Stakes Models
Building machine learning models in high-stakes contexts like finance, healthcare, and critical infrastructure often demands robustness, explainability, and other domain-specific constraints.
Everything You Need to Know About LLM Evaluation Metrics
When large language models first came out, most of us were just thinking about what they could do, what problems they could solve, and how far they might go.
The 7 Statistical Concepts You Need to Succeed as a Machine Learning Engineer
When we ask ourselves the question, ” what is inside machine learning systems? “, many of us picture frameworks and models that make predictions or perform tasks.
RL without TD learning
In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks. We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal …
What exactly does word2vec learn?
What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task. Despite the fact that word2vec is a well-known precursor to modern language models, for many years, researchers lacked a quantitative and predictive theory describing its learning process. In our new paper, we …