An updated overview of the Transformer model family, covering improvements for longer attention spans, efficiency, and new architectures since 2020.
A data-driven blog's thank you post, sharing visitor stats, popular posts on ML topics, and future plans for guest contributions.
A guide to deploying and understanding H2O's distributed machine learning platform on a Kubernetes cluster, focusing on its stateful architecture.
A guide to streamlining ML experiments by combining Jupyter, Papermill, and MLflow for parameterized runs and centralized logging.
A curated list of top data annotation companies worldwide, grouped by annotation type, focusing on services for computer vision, NLP, and audio data.
Analyzes the potential impact of the COVID-19 pandemic on major machine learning conferences, discussing outbreak scenarios and contingency plans.
A developer shares progress from the first week of an F# mentorship, covering a full-stack web project and machine learning experiments for March Madness predictions.
Explores the human effort behind AI training data, covering challenges of data annotation and techniques like transfer learning to reduce labeling workload.
A curated list of open-source and free tools for data annotation across computer vision, NLP, audio, and other domains, including image and video labeling.
A team built a handwritten sign digitizer for Hack Zurich 2016, creating a custom dataset and training a random forest image classifier in one day.
A personal blog about machine learning, data annotation projects, and professional experiences in deep learning and AI product development.
A developer shares their experience participating in the free F# mentorship program, both as a mentee and a mentor, and encourages others to join.
Explores curriculum learning strategies for training reinforcement learning models more efficiently, from simple to complex tasks.
A developer shares their personal routine of waking at 5 AM to study algorithms, data structures, Python, and machine learning to advance their tech career.
Survey of experimental methods used by authors at NeurIPS 2019 and ICLR 2020, focusing on hyperparameter tuning, baselines, and reproducibility.
A developer shares their personal learning journey and syllabus for mastering Python, Machine Learning, and Deep Learning in 2020.
H2O version 3.28.0.1 introduces parallel grid search for faster, concurrent hyperparameter tuning in distributed machine learning.
A summary of a meetup talk on advanced recommender systems, exploring techniques beyond baselines using graph and NLP methods.
Explores improving recommender systems using graph-based methods and NLP techniques like word2vec and DeepWalk in PyTorch.
A data scientist explores intellectual humility and reframing imposter syndrome as a learning alarm to improve professional well-being.
