In the rapidly evolving field of artificial intelligence, PyTorch has emerged as a beacon for developers, data scientists, and AI enthusiasts looking to dive deep into machine learning and deep learning. With its intuitive design, flexibility, and comprehensive library, PyTorch enables both newcomers and seasoned practitioners to advance the frontiers of AI research and application.
As we traverse our digital landscape, we leave trails in these tables that, if deciphered correctly, can unveil countless insights. Welcome to our book, Machine Learning on Tabular Data, a key guide to unlocking these mysteries. Dive in and navigate the ever-evolving dynamics of machine learning and deep learning, exclusively focused on this crucial data form. By the time you turn the last page, you’ll be prepared to transform columns and rows into actionable strategies and insights.
From Fight Fraud with Machine Learning by Ashish Ranjan Jha Step into the age of AI-powered fraud detection with Fight Fraud with Machine Learning, where every challenge is an opportunity to innovate. This comprehensive guide seamlessly blends theory with hands-on… Continue Reading →
From Engineering Deep Learning Systems by Chi Wang and Donald Szeto
This article presents what prospective readers can expect to learn from this book and why you should learn it.
Read it if you’re a software developer interested in transitioning your skills to the field of deep learning system design or an engineering-minded data scientist who want to build more effective delivery pipelines.
In this video, machine learning expert Eli Stevens showcases how to use open-source libraries that are available in the PyTorch ecosystem to cut down the amount of the code that you want to write.
Deep dive with Carl Osipov into understanding automatic differentiation used by PyTorch autograd for deep learning
From Serverless Machine Learning in Action by Carl Osipov
From Math and Architectures of Deep Learning by Krishnendu Chaudhury
By Robert Munro, author of Human-in-the-Loop Machine Learning