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 …
Whole-Body Conditioned Egocentric Video Prediction
× Predicting Ego-centric Video from human Actions (PEVA). Given past video frames and an action specifying a desired change in 3D pose, PEVA predicts the next video frame. Our results show that, given the first frame and a sequence of actions, our model can generate videos of atomic actions (a), simulate counterfactuals (b), and support …
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Essential Chunking Techniques for Building Better LLM Applications
Every large language model (LLM) application that retrieves information faces a simple problem: how do you break down a 50-page document into pieces that a model can actually use? So when you’re building a retrieval-augmented generation (RAG) app, before your vector database retrieves anything and your LLM generates responses, your documents need to be …
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How to Diagnose Why Your Language Model Fails
Language models , as incredibly useful as they are, are not perfect, and they may fail or exhibit undesired performance due to a variety of factors, such as data quality, tokenization constraints, or difficulties in correctly interpreting user prompts.
10 Python One-Liners for Calculating Model Feature Importance
Understanding machine learning models is a vital aspect of building trustworthy AI systems.
7 Prompt Engineering Tricks to Mitigate Hallucinations in LLMs
Large language models (LLMs) exhibit outstanding abilities to reason over, summarize, and creatively generate text.
7 Machine Learning Projects to Land Your Dream Job in 2026
machine learning continues to evolve faster than most can keep up with.