A curated list of public dataset repositories for machine learning and deep learning projects, including sources for computer vision, NLP, and more.
A behind-the-scenes look at designing and implementing a production machine learning system for a major hospital group, covering architecture and validation.
Announcing the 2021 Ihaka Lectures featuring local experts on distributed computing, machine learning for child welfare, and applied math for COVID-19 response.
A data science leader shares insights on hiring, training, and managing effective data science teams, based on experience at Lazada and uCare.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Argues that taking more MOOCs has diminishing returns for tech professionals and advocates for hands-on, project-based learning instead.
A review of the book 'Deep Learning with PyTorch', covering its structure, content, and suitability for students and beginners in deep learning.
A detailed review of the book 'Deep Learning with PyTorch,' covering its structure, content, and suitability for students and practitioners.
A podcast transcript discussing the importance of writing for career growth in tech, covering motivation, process, and Amazon's writing culture.
A technical deep dive into real-time machine learning for recommendation systems, comparing approaches in China and the US and discussing implementation.
Explores the connection between machine learning and statistics by building a statistical inference model from a neural network example.
A data scientist's 2020 review, focusing on machine learning projects for healthcare, including mining COVID-19 EHR data and brain signal analysis.
A data scientist's 2020 reflection on moving to Amazon, building ML systems, and establishing a weekly writing habit for learning and sharing knowledge.
Explores the difference between inference and prediction in data modeling, using a Click Through Rate (CTR) example to contrast Machine Learning and Statistics.
Analyzes the fallout from Timnit Gebru's firing from Google and debates appropriate community responses in the AI research field.
Analyzes three experiments on the ICML 2020 peer-review process, focusing on resubmission bias, discussion effects, and reviewer recruiting.
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.
