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.
A review of 'Architects of Intelligence,' a book featuring interviews with 23 leading AI researchers and industry experts.
A review of 'Architects of Intelligence,' a book featuring interviews with 23 leading AI researchers and industry experts.
A guide to building a recommender system using PyTorch on a laptop, covering data acquisition, parsing, and multiple modeling techniques.
A summary of the Global AI Bootcamp 2019 in Malmö, featuring presentations on practical AI use cases like genealogy, robotics, and construction.
Overview of the Skåne Azure User Group's active participation in Global AI Community events like Bootcamps and AI Nights since 2018.
Author announces the 3rd edition of Python Machine Learning, featuring TensorFlow 2.0 updates and a new chapter on Generative Adversarial Networks.
Announcing the 3rd edition of Python Machine Learning, updated for TensorFlow 2.0 and featuring a new chapter on Generative Adversarial Networks (GANs).
Explains how to import, inspect, and use H2O's MOJO models for scoring and analysis within the H2O platform.
Authors of scikit-learn receive a major scientific prize, highlighting a cultural shift towards recognizing open-source software as valuable academic contribution.
A review of Janelle Shane's AI humor book, discussing neural network limitations and the real-world impact of class imbalance in machine learning.
