Announcing a new book on machine learning, covering fundamentals with scikit-learn and deep learning with PyTorch, including neural networks from scratch.
Explores active learning strategies for selecting the most valuable data to label when working with a limited labeling budget in machine learning.
A developer built a program that uses webcam emotion detection to send cat pictures to their phone when they're sad at the computer.
A technical guide on deploying a DistilBERT model to production using Hugging Face Transformers, Amazon SageMaker, and Infrastructure as Code with Terraform.
Explains data annotation, its importance for training machine learning models, and details common text and image annotation types.
Final part of a series proposing a research agenda for ML monitoring, focusing on data management challenges like metric computation and real-time SLI tracking.
A developer builds a Figma plugin prototype using TensorFlow.js and hand gesture recognition to control UI design with hand movements.
Practical strategies for staying current in the fast-moving field of machine learning, including project experimentation and community engagement.
A comprehensive collection of 90 machine learning lecture videos covering Python, scikit-learn, algorithms, and model evaluation techniques.
A comprehensive list of 90 machine learning lecture videos covering topics from Python basics to advanced ML concepts like decision trees and Bayesian methods.
Introducing PairwiseDistancesReduction, a new Cython-based abstraction in scikit-learn for high-performance CPU computations of reductions over pairwise distances.
Guide to deploying Hugging Face Transformer models using Amazon SageMaker Serverless Inference for cost-effective ML prototypes.
Explains ongoing developer efforts to dramatically improve scikit-learn's performance, focusing on hardware scalability and algorithmic optimizations.
Analyzes post-deployment ML issues and categorizes them to advocate for better monitoring tools, using Zillow's case as an example.
Explores semi-supervised learning techniques for training models when labeled data is scarce, focusing on combining labeled and unlabeled data.
The author introduces ApplyingML.com, a site dedicated to sharing practical knowledge and interviews on applying machine learning effectively in real-world work.
Learn how to integrate the Hugging Face Hub as a model registry with Amazon SageMaker for MLOps, including training and deployment.
A guide to attending AWS re:Invent 2021 machine learning and NLP sessions remotely, featuring keynotes and top session recommendations.
A tutorial on deploying the BigScience T0_3B language model to AWS and Amazon SageMaker for production use.
Advises starting ML projects with simple heuristics and data analysis before implementing complex machine learning models, citing expert advice.
