An interview with lead data scientist Alexey Grigorev on his career transition from software engineering to data science, his advice, and his work at OLX.
Explains the Neural Tangent Kernel concept through simple 1D regression examples to illustrate how neural networks evolve during training.
A tutorial on building a serverless question-answering API using BERT, Hugging Face, AWS Lambda, and EFS to overcome dependency and model load limitations.
Explains the concept of causally correct partial models for reinforcement learning in POMDPs, focusing on counterfactual policy evaluation.
Explains the differences between Applied Scientist, Research Scientist, and ML Engineer roles in data science and machine learning.
Introducing efsync, an open-source MLOps toolkit for syncing dependencies and model files to AWS EFS for serverless machine learning.
An interview with Chip Huyen about her journey from a small village to Stanford and a career in ML, her writing, and thoughts on machine learning in production.
An analysis of GPT-3's capabilities, potential for misuse in generating fake news and spam, and its exclusive licensing by Microsoft.
Explains the theory behind linear regression models, a fundamental machine learning technique for predicting continuous numerical values.
A step-by-step guide to installing Google's ScaNN library for efficient vector similarity search on macOS, covering dependencies and troubleshooting.
Explains how building a simple prototype can be more effective than proposals for gaining stakeholder buy-in on tech projects.
Explains the theory behind linear regression models, focusing on interpretability and use cases in fields like lending and medicine.
An interview with an Amazon Applied Scientist describing the daily work, challenges, and projects involved in building ML systems like book recommendations.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A guide to testing machine learning code and systems, covering pre-train and post-train tests, evaluation, and implementation with a DecisionTree example.
A developer asks when to use ML for parsing PDF fields with typos, and receives advice on using Levenshtein distance and human-in-the-loop solutions.
A podcast interview with data scientist Eugene Yan discussing his career transition, data science leadership, and experiences at Lazada.
Explains how regularly reading academic papers improves data science skills, offering practical advice on selection and application.
A review and tutorial on interpretable machine learning, covering Christoph Molnar's book and providing Python code examples for linear/logistic regression.
A review and tutorial covering Christoph Molnar's book on Interpretable Machine Learning, with Python code examples for linear and logistic regression.
