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Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. While the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required.
Cellular Nonlinear/Neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed. This unique undergraduate level textbook includes many examples and exercises, including CNN simulator and development software accessible via the Internet. It is an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of disciplines. Leon Chua, co-inventor of the CNN, and Tamàs Roska are both highly respected pioneers in the field.
The use of neural networks in signal processing is becoming
increasingly widespread, with applications in many areas. Applied
Neural Networks for Signal Processing is the first book to provide
a comprehensive introduction to this broad field. It begins by
covering the basic principles and models of neural networks in
signal processing. The authors then discuss a number of powerful
algorithms and architectures for a range of important problems, and
describe practical implementation procedures. A key feature of the
book is that many carefully designed simulation examples are
included to help guide the reader in the development of systems for
new applications. The book will be an invaluable reference for
scientists and engineers working in communications, control or any
other field related to signal processing. It can also be used as a
textbook for graduate courses in electrical engineering and
computer science.
A concise and self-contained introduction to causal inference,
increasingly important in data science and machine learning. The
mathematization of causality is a relatively recent development,
and has become increasingly important in data science and machine
learning. This book offers a self-contained and concise
introduction to causal models and how to learn them from data.
After explaining the need for causal models and discussing some of
the principles underlying causal inference, the book teaches
readers how to use causal models: how to compute intervention
distributions, how to infer causal models from observational and
interventional data, and how causal ideas could be exploited for
classical machine learning problems. All of these topics are
discussed first in terms of two variables and then in the more
general multivariate case. The bivariate case turns out to be a
particularly hard problem for causal learning because there are no
conditional independences as used by classical methods for solving
multivariate cases. The authors consider analyzing statistical
asymmetries between cause and effect to be highly instructive, and
they report on their decade of intensive research into this
problem. The book is accessible to readers with a background in
machine learning or statistics, and can be used in graduate courses
or as a reference for researchers. The text includes code snippets
that can be copied and pasted, exercises, and an appendix with a
summary of the most important technical concepts.
Visualize and build deep learning models with 3D data using
PyTorch3D and other Python frameworks to conquer real-world
application challenges with ease Key Features Understand 3D data
processing with rendering, PyTorch optimization, and heterogeneous
batching Implement differentiable rendering concepts with practical
examples Discover how you can ease your work with the latest 3D
deep learning techniques using PyTorch3D Book DescriptionWith this
hands-on guide to 3D deep learning, developers working with 3D
computer vision will be able to put their knowledge to work and get
up and running in no time. Complete with step-by-step explanations
of essential concepts and practical examples, this book lets you
explore and gain a thorough understanding of state-of-the-art 3D
deep learning. You'll see how to use PyTorch3D for basic 3D mesh
and point cloud data processing, including loading and saving ply
and obj files, projecting 3D points into camera coordination using
perspective camera models or orthographic camera models, rendering
point clouds and meshes to images, and much more. As you implement
some of the latest 3D deep learning algorithms, such as
differential rendering, Nerf, synsin, and mesh RCNN, you'll realize
how coding for these deep learning models becomes easier using the
PyTorch3D library. By the end of this deep learning book, you'll be
ready to implement your own 3D deep learning models confidently.
What you will learn Develop 3D computer vision models for
interacting with the environment Get to grips with 3D data handling
with point clouds, meshes, ply, and obj file format Work with 3D
geometry, camera models, and coordination and convert between them
Understand concepts of rendering, shading, and more with ease
Implement differential rendering for many 3D deep learning models
Advanced state-of-the-art 3D deep learning models like Nerf,
synsin, mesh RCNN Who this book is forThis book is for beginner to
intermediate-level machine learning practitioners, data scientists,
ML engineers, and DL engineers who are looking to become
well-versed with computer vision techniques using 3D data.
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