A technical tutorial on implementing Thompson Sampling to optimize ad auction decisions by balancing bid values and click-through rates.
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.
Article discusses the 'expert beginner' trap in tech, where narrow success halts learning, and advocates for maintaining a beginner's mindset.
A chronological survey of key NLP models and techniques for supervised learning, from early RNNs to modern transformers like BERT and T5.
A tutorial on building a GitHub Action that uses TensorFlow.js to automatically detect toxic comments and PR reviews in a repository.
A tutorial on integrating AWS EFS storage with AWS Lambda functions using the Serverless Framework, focusing on overcoming storage limits for serverless applications.
An overview of Neural Architecture Search (NAS), covering its core components: search space, algorithms, and evaluation strategies for automating AI model design.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and the shift from traditional programming.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and terminology from a university course.
A personal analysis of the pros and cons for CS grads choosing between pursuing a PhD or entering the tech industry, focusing on machine learning careers.
Notes from Spark+AI Summit 2020 covering application-specific talks on ML frameworks, data engineering, feature stores, and data quality from companies like Airbnb and Netflix.
Summary of key application-agnostic talks from Spark+AI Summit 2020, focusing on scaling and optimizing deep learning models.
