Machine Learning Q and AI

AFTERWORD

This book has taken you on a journey that began with the foundational concepts of machine learning, such as embeddings, latent spaces, and representations, and advanced techniques and architectures, including self-supervised learning, few-shot learning, and transformers. It also covered many practical techniques, such as model deployment, multi-GPU training, and data-centric AI, and dove into specialized domains like computer vision and natural language processing.

Each chapter of this book has not only equipped you with conceptual knowledge, but also offered practical insights, making this book useful for both academic and real-world ...