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UX for Lean Startups
Author: Laura Klein
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2018-11-16
p>Great user experiences (UX) are essential for products today, but designing one can be a lengthy and expensive process. With this practical, hands-on book, you’ll learn how to do it faster and smarter using Lean UX techniques. UX expert Laura Klein shows you what it takes to gather valuable input from customers, build something they’ll truly love, and reduce the time it takes to get your product to market. No prior experience in UX or design is necessary to get started. If you’re an entrepreneur or an innovator, this book puts you right to work with proven tips and tools for researching, identifying, and designing an intuitive, easy-to-use product. Determine whether people will buy your product before you build it Listen to your customers throughout the product’s lifecycle Understand why you should design a test before you design a product Get nine tools that are critical to designing your product Discern the difference between necessary features and nice-to-haves Learn how a Minimum Viable Product affects your UX decisions Use A/B testing in conjunction with good UX practices Speed up your product development process without sacrificing quality
Deep Learning
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Opening Science
Modern information and communication technologies, together with a cultural upheaval within the research community, have profoundly changed research in nearly every aspect. Ranging from sharing and discussing ideas in social networks for scientists to new collaborative environments and novel publication formats, knowledge creation and dissemination as we know it is experiencing a vigorous shift towards increased transparency, collaboration and accessibility. Many assume that research workflows will change more in the next 20 years than they have in the last 200. This book provides researchers, decision makers, and other scientific stakeholders with a snapshot of the basics, the tools, and the underlying visions that drive the current scientific (r)evolution, often called ‘Open Science.’