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Mass Effect™: Andromeda Initiative Backpack: A two-pocket Andromeda Initiative backpack that holds a 15-inch laptop.
Alternate Premium Hardcover Guide: An exclusive hardcover version of the complete guide. A must-have for every Mass Effect fan! Only available in the Pathfinder Edition.
DLC Code Inside: Get a head start on Day 1 co-op play with the MultiPlayer Booster Pack, which includes weapons and equipment to kick-start your progress (entitled instantly, limit one per match).
Welcome Letter: An introduction letter, written by the mission’s founder, Jien Garson, welcomes you to the Andromeda Initiative.
Galaxy Chart: A full-color 11”x17” map of the Andromeda Galaxy.
Field Journal: A 32-page journal with field notes and sketches about the Initiative with space for your own note-taking needs while on your adventure.
Branded Envelope: The Galaxy Chart and Welcome Letter come packaged in an Andromeda Initiative branded envelope.
Mobile-Friendly eGuide: Unlock the enhanced eGuide for strategy on the go, all optimized for a second-screen experience.
Limited quantities! Once this Pathfinder Edition box is sold out, it will no longer be available, making this a must-have for collectors!
"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.