A curated list and summary of recent research papers exploring deep learning methods specifically designed for tabular data.
A roboticist argues for scaling robotics research like generative AI, focusing on data quality and iteration over algorithms for better generalization.
Explores using DALL·E 2 AI to generate symbolic and spiritual art, with tips on prompts like 'religious art' for creative results.
Examines the common practice of using powers of 2 for neural network batch sizes, questioning its necessity with practical and theoretical insights.
Challenges the common practice of using powers of 2 for neural network batch sizes, examining the theory and practical benchmarks.
Learn how to fine-tune the XLM-RoBERTa model for multilingual text classification using Hugging Face libraries on cost-efficient Habana Gaudi AWS instances.
Learn how to deploy a deep learning research demo on the cloud using the Lightning framework, including GPU training and model sharing.
A guide to deploying and sharing deep learning research demos on the cloud using the Lightning framework, including model training.
A tutorial on building a Super Resolution GAN demo app using the Lightning framework to share deep learning research models.
Learn to build a Super Resolution GAN demo using the Lightning framework in this first part of a deep learning tutorial series.
Guide to setting up a deep learning environment on AWS using Habana Gaudi accelerators and Hugging Face libraries for transformer models.
A hands-on review of PyTorch's new M1 GPU support, including installation steps and performance benchmarks for deep learning tasks.
A hands-on review and benchmark of PyTorch's new official GPU support for Apple's M1 chips, covering installation and performance.
A deep learning researcher shares insights on the 2022 ML job market, comparing career options like FAANG, startups, and robotics, after joining Halodi Robotics.
Explains cross-entropy loss in PyTorch for binary and multiclass classification, highlighting common implementation pitfalls and best practices.
Explains the difference between .update() and .forward() in TorchMetrics, a PyTorch library for tracking model performance during training.
Explains the difference between .update() and .forward() methods in the TorchMetrics library for evaluating PyTorch models.
Explores how Large Language Models perform implicit Bayesian inference through in-context learning, connecting exchangeable sequence models to prompt-based learning.
Announcing a new book on machine learning, covering fundamentals with scikit-learn and deep learning with PyTorch, including neural networks from scratch.
Author announces a new machine learning book covering scikit-learn, deep learning with PyTorch, neural networks, and reinforcement learning.
