O'Reilly Media

An understanding of psychology-specifically the psychology behind how users behave and interact with digital interfaces-is perhaps the single most valuable nondesign skill a designer can have. The most elegant design can fail if it forces users to conform to the design instead of working within the "blueprint" of how humans perceive and process the world around them. This practical guide explains how you can apply key principles of psychology to build products and experiences that are more human-centered and intuitive. Author Jon Yablonski deconstructs familiar apps and experiences to provide clear examples of how UX designers can build interfaces that adapt to how users perceive and process digital interfaces. You'll learn: How aesthetically pleasing design creates positive responses The principles of psychology most useful for designers How these psychology principles relate to UX heuristics Predictive models including Fitts's law, Jakob's law, and Hick's law Ethical implications of using psychology in design A practical framework for applying principles of psychology in your design process This updated edition includes an even deeper connection to the underlying psychological concepts that govern the principles explored in the book, along with accompanying UX methods and techniques. Examples have been updated to ensure the deconstructed apps and experiences remain familiar and relevant.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets