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Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. The author introduces the basic principles of pattern recognition and then goes on to describe techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. This book is designed with graduate students in mind and throughout the text it motivates the use of various forms of error functions and reviews the principal algorithms for error function minimization. Bishop also covers the fundamental topics of data processing, feature extraction, and prior knowledge and concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
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.
Master advanced techniques and algorithms for machine learning with
PyTorch using real-world examples Key Features * Understand how to
use PyTorch to build advanced neural network models including graph
neural networks and reinforcement learning models * Learn the
latest tech, such as generating images from text using diffusion
models * Become an expert in deploying PyTorch models in the cloud,
on mobile and across platforms * Get the best from PyTorch by
working with key libraries, including Hugging Face, fast.ai, and
PyTorch Lightning Book Description PyTorch is making it easier than
ever before for anyone to build deep learning applications. This
PyTorch book will help you uncover expert techniques to get the
most from your data and build complex neural network models. You'll
create convolutional neural networks (CNNs) for image
classification and recurrent neural networks (RNNs) and
transformers for sentiment analysis. As you advance, you'll apply
deep learning across different domains, such as music, text, and
image generation using generative models. You'll not only build and
train your own deep reinforcement learning models in PyTorch but
also deploy PyTorch models to production, including mobiles and
embedded devices. Finally, you'll discover the PyTorch ecosystem
and its rich set of libraries. These libraries will add another set
of tools to your deep learning toolbelt, teaching you how to use
fast.ai for prototyping models to training models using PyTorch
Lightning. You'll discover libraries for AutoML and explainable AI,
create recommendation systems using TorchRec, and build language
and vision transformers with Hugging Face. By the end of this
PyTorch book, you'll be able to perform complex deep learning tasks
using PyTorch to build smart artificial intelligence models. What
you will learn * Implement text, image, and music generating models
using PyTorch * Build a deep Q-network (DQN) model in PyTorch *
Deploy PyTorch models on mobiles and embedded devices * Become
well-versed with rapid prototyping using PyTorch with fast.ai *
Perform neural architecture search effectively using AutoML *
Easily interpret machine learning models using Captum * Develop
your own recommendation system using TorchRec * Design ResNets,
LSTMs, and graph neural networks * Create language and vision
transformer models using Hugging Face Who This Book Is For This
book is for data scientists, machine learning researchers, and deep
learning practitioners looking to implement advanced deep learning
models using PyTorch. This book is an ideal resource for those
looking to switch from TensorFlow to PyTorch. Working knowledge of
deep learning with Python programming is required.
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.
Discover recipes for developing AI applications to solve a variety
of real-world business problems using reinforcement learning Key
Features Develop and deploy deep reinforcement learning-based
solutions to production pipelines, products, and services Explore
popular reinforcement learning algorithms such as Q-learning,
SARSA, and the actor-critic method Customize and build RL-based
applications for performing real-world tasks Book DescriptionWith
deep reinforcement learning, you can build intelligent agents,
products, and services that can go beyond computer vision or
perception to perform actions. TensorFlow 2.x is the latest major
release of the most popular deep learning framework used to develop
and train deep neural networks (DNNs). This book contains
easy-to-follow recipes for leveraging TensorFlow 2.x to develop
artificial intelligence applications. Starting with an introduction
to the fundamentals of deep reinforcement learning and TensorFlow
2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and
how to develop basic agents. You'll discover how to implement
advanced deep reinforcement learning algorithms such as
actor-critic, deep deterministic policy gradients, deep-Q networks,
proximal policy optimization, and deep recurrent Q-networks for
training your RL agents. As you advance, you'll explore the
applications of reinforcement learning by building cryptocurrency
trading agents, stock/share trading agents, and intelligent agents
for automating task completion. Finally, you'll find out how to
deploy deep reinforcement learning agents to the cloud and build
cross-platform apps using TensorFlow 2.x. By the end of this
TensorFlow book, you'll have gained a solid understanding of deep
reinforcement learning algorithms and their implementations from
scratch. What you will learn Build deep reinforcement learning
agents from scratch using the all-new TensorFlow 2.x and Keras API
Implement state-of-the-art deep reinforcement learning algorithms
using minimal code Build, train, and package deep RL agents for
cryptocurrency and stock trading Deploy RL agents to the cloud and
edge to test them by creating desktop, web, and mobile apps and
cloud services Speed up agent development using distributed DNN
model training Explore distributed deep RL architectures and
discover opportunities in AIaaS (AI as a Service) Who this book is
forThe book is for machine learning application developers, AI and
applied AI researchers, data scientists, deep learning
practitioners, and students with a basic understanding of
reinforcement learning concepts who want to build, train, and
deploy their own reinforcement learning systems from scratch using
TensorFlow 2.x.
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