300 Things to Know About Machine Learning Frameworks

This comprehensive book serves as a one-stop source for essential terms and definitions in the field of machine learning frameworks. The book is written in a glossary-style format, allowing readers to easily access and navigate through the vast world of machine learning.

From the basics of classification and regression to more advanced concepts such as neural networks and deep learning, this book covers it all. Key terms and definitions are explained in a concise and simple language, making it an ideal reference material for students, researchers, and professionals in the field of artificial intelligence.

The book features over hundreds of terms relating to machine learning and artificial intelligence, including commonly used terms such as overfitting, underfitting, and bias, as well as more specialized terminologies like autoencoder, reinforcement learning, and transfer learning. With its in-depth coverage of machine learning frameworks, this book is an indispensable tool for anyone who wants to stay up-to-date with the latest developments in this rapidly evolving field.