Consensualism in Principle: On the Foundations of Non-Consequentialist Moral Reasoning (Studies in Ethics)

This book presents and argues for a suitably articulated version of consensualism as a form of Kantian moral theory with an ability to powerfully illuminate the moral intuitions to which Kantian and utilitarian theories have traditionally appealed.

Politics & Social Sciences

Kumar, Rahul

Philosophy

Routledge

Poem book about the highest thoughts of Upnishadas and Love .

Yajnavalkya and Some Love Poems (Musings on Vedanta)

Literature & Fiction

Poetry

CreateSpace Independent Publishing Platform

Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features Your guide to learning efficient machine learning processes from scratch  Explore expert techniques and hacks for a variety of machine learning concepts  Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems   Book Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner.  After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered.  By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.  What you will learn Get a quick rundown of model selection, statistical modeling, and cross-validation  Choose the best machine learning algorithm to solve your problem  Explore kernel learning, neural networks, and time-series analysis  Train deep learning models and optimize them for maximum performance  Briefly cover Bayesian techniques and sentiment analysis in your NLP solution  Implement probabilistic graphical models and causal inferences  Measure and optimize the performance of your machine learning models Who this book is for If you're a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you're an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You'll need some exposure to machine learning to get the best out of this book.Table of Contents Quantifying Learning Algorithms Evaluating Kernel Learning  Performance in Ensemble Learning  Training Neural Networks  Time-Series Analysis  Natural Language Processing  Temporal and Sequential Pattern Discovery Probabilistic Graphical Models  Selected Topics in Deep Learning  Causal Inference  Advanced Methods

Machine Learning Quick Reference

Computers & Technology

Computer Science

Packt Publishing

Insightful projects to master deep learning and neural network architectures using Python and Keras Key Features <ul>
<li>Explore deep learning across computer vision, natural language processing (NLP), and image processing </li> <li>Discover best practices for the training of deep neural networks and their deployment </li> <li>Access popular deep learning models as well as widely used neural network architectures </li> </ul> Book Description Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You'll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you'll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you'll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learn <ul>
<li>Set up a deep learning development environment on Amazon Web Services (AWS) </li> <li>Apply GPU-powered instances as well as the deep learning AMI </li> <li>Implement seq-to-seq networks for modeling natural language processing (NLP) </li> <li>Develop an end-to-end speech recognition system </li> <li>Build a system for pixel-wise semantic labeling of an image </li> <li>Create a system that generates images and their regions</li>
</ul> Who this book is for Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming Table of Contents <ol>
<li>Building Deep Learning Environment</li> <li>Training Neural Network for Prediction using Regression</li> <li>Word Vector representationusing Word2VEC (skip-gram) for word prediction</li> <li>Build NLP pipeline for Open-Domain Question Answering</li> <li>Sequence-to-sequence models for building chatbots</li> <li>Generative Language modelling using Bi-LSTM for content creation</li> <li>Building Speech Recognition with DeepSpeech2</li> <li>Handwritten digits classification using ConvNets</li> <li>Real-time Object Detection using OpenCV and TensorFlow</li> <li>Building Face Recognition using OpenFace and Clustering</li> <li>Automated Image Captioning with NeuralTalk model</li> <li>Pose Estimation on 3D models using ConvNets</li> <li>Image translation using GANs for style transfer</li> <li>Develop anautonomous Agents with Deep Reinforcement Learning</li> <li>Summary and Next Steps in Your Deep Learning Career</li>
</ol>

Python Deep Learning Projects

Lamons, Matthew

Nagaraja, Abhishek

Develop real-world applications powered by the latest advances in intelligent systems Key Features <ul>
<li>Gain real-world contextualization using deep learning problems concerning research and application </li> <li>Get to know the best practices to improve and optimize your machine learning systems and algorithms </li> <li>Design and implement machine intelligence using real-world AI-based examples </li> </ul> Book Description This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries. Throughout the Learning Path, you'll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way. By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects. This Learning Path includes content from the following Packt products: <ul>
<li>Artificial Intelligence By Example by Denis Rothman</li> <li>Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja</li> <li>Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Dixit</li> </ul> What you will learn <ul>
<li>Use adaptive thinking to solve real-life AI case studies </li> <li>Rise beyond being a modern-day factory code worker </li> <li>Understand future AI solutions and adapt quickly to them </li> <li>Master deep neural network implementation using TensorFlow </li> <li>Predict continuous target outcomes using regression analysis </li> <li>Dive deep into textual and social media data using sentiment analysis </li> </ul> Who this book is for This Learning Path is for anyone who wants to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. You will learn to extend your machine learning and deep learning knowledge by creating practical AI smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this Learning Path. Table of Contents <ol>
<li>Become an Adaptive Thinker</li> <li>Think Like a Machine</li> <li>Apply Machine Thinking to a Human Problem </li> <li>Become an Unconventional Innovator</li> <li>Manage the Power of Machine Learning and Deep Learning</li> <li>Focus on Optimizing Your Solutions</li> <li>When and How to Use Artificial Intelligence</li> <li>Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies</li> <li>Getting Your Neurons to Work</li> <li>Applying Biomimicking to Artificial Intelligence</li> <li>Conceptual Representation Learning</li> <li>Optimizing Blockchains with AI</li> <li>Cognitive NLP Chatbots</li> <li>Improve the Emotional Intelligence Deficiencies of Chatbots</li> <li>Building Deep Learning Environments</li> <li>Training NN for Prediction Using Regression</li> <li>Generative Language Model for Content Creation</li> <li>Building Speech Recognition with DeepSpeech2</li> <li>Handwritten Digits Classification Using ConvNets</li> <li>Object Detection Using OpenCV and TensorFlow</li> <li>Building Face Recognition Using FaceNet</li> <li>Generative Adversarial Networks</li> <li>From GPUs to Quantum computing - AI Hardware</li> <li>TensorFlow Serving</li>
</ol>

Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python

Rothman, Denis

Ziai, Amir

Dixit, Ankit

Football and Fascism. The Politics of Popular Culture in Portugal tells the hidden history of football and discusses its political, social and cultural foundations, during the longest running authoritarian regime in Europe. Theoretically grounded on Bourdieu's field theory, and using a multi-scalar methodology, this award-winning research explores the political tensions between the nationalization of sports envisaged by the Portuguese "New State" and the integration of national football in a globalized urban popular culture. Mobilizing unexplored archival sources, and a wide array of primary materials, this groundbreaking work offers new insight on the administrative structures of the corporativist state, the making of an authoritarian cultural program, and the relation between state institutions and civil society. Besides broadening the scope of existing transnational histories of football, this study also puts into question the conventional geographies and political chronologies adopted in sports history. For his oustanding research, Rahul Kumar won the 2015 "Mário Soares Award - EDP Foundation" for best work in Portuguese history by researchers under 35 and received an honourable mention, also in 2015, in the "CES Award for Young Portuguese speaking Social Scientists", attributed by the Centre for Social Studies of Coimbra University.

Football and Fascism: The Politics of Popular Culture in Portugal (RERIS Studies in International Sport Relations, 2)

History

Europe

De Gruyter Oldenbourg

This book is written with a prime goal to provide guidance to veterinary professionals with particular reference to those working in field of veterinary forensic science. The book addresses several issues of animal abuse and put forth the possible linkage between animal and human violence. It also highlights the importance of Veterinarians in animal cruelty investigations and recommends their collaboration with professionals from other fields. Besides investigators, the book is expected to benefit field veterinarians and students as well.

Veterinary Forensic Medicine: Veterinary Jurisprudence

Medical Books

Rajesh, J. B.

Veterinary Medicine

Scholars' Press

Machine Learning Using R

U Dinesh Kumar

Arihant Test Drive 15 Practice Sets For JEE Main 2024

Education & Teaching

Chaudhary, Anurag

Yadav, Pooja

Studying & Workbooks

Arihant Publication India Limited

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