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 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.
The article argues that the choice of machine learning library (like PyTorch or TensorFlow) is less critical than building robust data and production pipelines.
Summary of key application-agnostic talks from Spark+AI Summit 2020, focusing on scaling and optimizing deep learning models.
A step-by-step tutorial on deploying a custom PyTorch machine learning model to production using AWS Lambda and the Serverless Framework.
A comparative analysis of the underlying architecture and design principles of TensorFlow and PyTorch machine learning frameworks.
Explores improving recommender systems using graph-based methods and NLP techniques like word2vec and DeepWalk in PyTorch.
A guide to building a recommender system using PyTorch on a laptop, covering data acquisition, parsing, and multiple modeling techniques.
A satirical look at AI development and government funding, imagining a fictional 'Ministry of Silly Models' in the UK.
An annotated, line-by-line implementation of the Transformer architecture from 'Attention is All You Need' in PyTorch.
A comparison of PyTorch and TensorFlow deep learning frameworks, focusing on programmability, flexibility, and ease of use for different project scales.
