A retrospective on forming a research team in 2022 to apply machine learning to challenges in health and social sciences, including data management and validation.
Learn how to train an XGBoost classifier using cloud GPUs without managing infrastructure via the Lightning AI framework.
A curated list of the top 10 open-source machine learning and AI projects released or updated in 2022, including PyTorch 2.0 and scikit-learn 1.2.
A review of the top 10 most influential machine learning papers from 2022, including ConvNeXt and MaxViT, with technical analysis.
Author announces the launch of 'Ahead of AI', a monthly newsletter covering AI trends, educational content, and personal updates on machine learning projects.
A curated list and summary of recent research papers exploring deep learning methods specifically designed for tabular data.
A guide to creating confidence intervals for evaluating machine learning models, covering multiple methods to quantify performance uncertainty.
Author announces a new machine learning book covering scikit-learn, deep learning with PyTorch, neural networks, and reinforcement learning.
A comprehensive collection of 90 machine learning lecture videos covering Python, scikit-learn, algorithms, and model evaluation techniques.
A comprehensive deep learning course covering fundamentals, neural networks, computer vision, and generative models using PyTorch.
A curated list of public dataset repositories for machine learning and deep learning projects, including computer vision and NLP datasets.
A review of the book 'Deep Learning with PyTorch', covering its structure, content, and suitability for students and beginners in deep learning.
A data scientist's 2020 review, focusing on machine learning projects for healthcare, including mining COVID-19 EHR data and brain signal analysis.
An analysis of GPT-3's capabilities, potential for misuse in generating fake news and spam, and its exclusive licensing by Microsoft.
A review and tutorial on interpretable machine learning, covering Christoph Molnar's book and providing Python code examples for linear/logistic regression.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and the shift from traditional programming.
Survey of experimental methods used by authors at NeurIPS 2019 and ICLR 2020, focusing on hyperparameter tuning, baselines, and reproducibility.
A review of 'Architects of Intelligence,' a book featuring interviews with 23 leading AI researchers and industry experts.
Author announces the 3rd edition of Python Machine Learning, featuring TensorFlow 2.0 updates and a new chapter on Generative Adversarial Networks.
Authors of scikit-learn receive a major scientific prize, highlighting a cultural shift towards recognizing open-source software as valuable academic contribution.
