An in-depth look at torchdata's internal architecture, focusing on datapipes and how they optimize data loading for PyTorch to improve GPU memory bandwidth.
Explores techniques for flattening and unflattening nested data structures in TensorFlow, JAX, and PyTorch for efficient deep learning model development.
A hands-on exploration of PyTorch's new DataPipes for efficient data loading, comparing them to traditional Datasets and DataLoaders.
A hands-on exploration of PyTorch's new DataPipes for efficient data loading, comparing them to traditional Datasets and DataLoaders.
Explores the application of classic software design patterns, like the Factory pattern, to machine learning code and systems, using examples from PyTorch, Gensim, and Hugging Face.
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 guide to correctly implementing cross-entropy loss in PyTorch for binary and multiclass classification, explaining common pitfalls and best practices.
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.
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.
A guide to accelerating multilingual BERT fine-tuning using Hugging Face Transformers with distributed training on Amazon SageMaker.
Practical strategies for staying current in the fast-moving field of machine learning, including project experimentation and community engagement.
A comprehensive deep learning course covering fundamentals, neural networks, computer vision, and generative models using PyTorch.
A comprehensive deep learning course overview with PyTorch tutorials, covering fundamentals, neural networks, and advanced topics like CNNs and GANs.
A detailed review of the book 'Deep Learning with PyTorch,' covering its structure, content, and suitability for students and practitioners.
A review of the book 'Deep Learning with PyTorch', covering its structure, content, and suitability for students and beginners in deep learning.
The article argues that the choice of machine learning library (like PyTorch or TensorFlow) is less critical than building robust data and production pipelines.
