This book covers both classical and modern models in deep learning. The chapters of this book span three categories:
The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional mac...
This book provides a unique study of the complexities and consequences of irregular legal status of Senegalese migrants in Europe. It employs sophisticated quantitative methods to analyze unique life-history data to produce policy-relevant conclusions. Using the MAFE dataset as empirical evidence, the book focuses on the legal paths of Senegalese ...
This open access book provides an alternative theoretical framework of irregular migration that allows to overcome many of the contradictions and theoretical impasses displayed by the majority of approaches in current literature. The analytical framework allows moving from an interpretation biased by methodological nationalism, to a more general sy...
James McCaffrey leads you through the fundamental concepts of neural networks, including their architecture, input-output, tanh and softmax activation, back-propagation, error and accuracy, normalization and encoding, and model interpretation. Although most concepts are relatively simple, there are many of them, and they interact with each other in...
AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising direc...
The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can "learn" from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training...