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.
Answers common questions about data science in business, covering requirements, model interpretability, web scraping, and team roles.
Explores the career choice between being a technology generalist or specialist, analyzing the pros and cons of each path in the evolving tech industry.
Announcement for the Azure Skåne AI Day event, featuring sessions on Azure Cognitive Search, AI monitoring, Custom Vision, and transfer learning.
A guide to best practices for monitoring, maintaining, and managing machine learning models and data pipelines in a production environment.
Explores six unexpected challenges that arise after deploying machine learning models in production, from data schema changes to organizational issues.
A data scientist shares their workflow using the {drake} R package to manage dependencies and ensure reproducibility in long-term machine learning projects.
Announcement for an online Azure Skåne User Group event featuring AI/ML sessions on predicting earthquake damage and building chatbots.
A tutorial on using AWS AutoGluon, an AutoML library, to build an object detection model with minimal code.
A comparative analysis of the underlying architecture and design principles of TensorFlow and PyTorch machine learning frameworks.
Explains K-Fold cross-validation for ML models with a practical example using BERT for text classification.
