Deep Learning Essentials


Book Description

Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.




Visual Attention Mechanisms


Book Description

Proceedings of the Fifth International School on Neural Networks "E.R. Caianiello" on Visual Attention MechaProceedings of the Fifth International School on Neural Networks "E.R. Caianiello" on Visual Attention Mechanisms, held 23-28 October 2000 in Vietri sul Mare, Italy.nisms, held 23-28 October 2000 in Vietri sul Mare, Italy. The book covers a number of broad themes relevant to visual attention, ranging from computer vision to psychology and physiology of vision. The main theme of the book is the attention processes of vision systems and it aims to point out the analogies and the divergences of biological vision with the frameworks introduced by computer scientists in artificial vision.




Mechanisms of Visual Attention


Book Description

In order to produce coherent behaviour in a complex world, forms of visual attention are necessary in order for us to select appropriate objects for action. Over the past ten years, there have been considerable advances in research into visual attention, with many of these advances linked to interdisciplinary research in experimental psychology, neuropsychology, neurophysiology and functional imaging. This work has begun to allow us to understand not only the functional properties of visual attention, but also how attentional processes are localized in the brain: the cognitive neuroscience of visual attention. This special issue draws together research from leading figures in this field, to highlight recent progress in understanding how selective processes operate in perception and action.




Visual Attention and Cortical Circuits


Book Description

An attempt to derive a comprehensive theory of attention from both neurobiological and psychological data.




Eye Movements and Visual Cognition


Book Description

Edited by a leading scholar in the field, Eye Movements and Visual Cognitionpresents an up-to-date overview of the topics relevant to understanding the relationship between eye movements and visual cognition, particularly in relation to scene perception and reading. Cognitive psychologists, neuropsychologists, educational psychologists, and reading specialists will find this volume to be an authoritative source of state-of-the art research in this rapidly expanding area of study.







Encyclopedia of Neuroscience


Book Description

This 5000-page masterwork is literally the last word on the topic and will be an essential resource for many. Unique in its breadth and detail, this encyclopedia offers a comprehensive and highly readable guide to a complex and fast-expanding field. The five-volume reference work gathers more than 10,000 entries, including in-depth essays by internationally known experts, and short keynotes explaining essential terms and phrases. In addition, expert editors contribute detailed introductory chapters to each of 43 topic fields ranging from the fundamentals of neuroscience to fascinating developments in the new, inter-disciplinary fields of Computational Neuroscience and Neurophilosophy. Some 1,000 multi-color illustrations enhance and expand the writings.




Visual Question Answering


Book Description

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.




Visual Attention Mechanisms


Book Description

Papers from an October 2000 workshop look at the attention processes of vision systems and point out the analogies and divergences of biological vision as compared to the frameworks introduced by computer scientists in artificial vision. Material is arranged in sections on foundations, attentional engagements, visual search, multiresolution and planning, and attentional visual architectures. Some specific topics include eye movement analysis during visual exploration of graphical interfaces, neural mechanisms of attentional control, and connectionist models of attentional processing. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com).




Attention


Book Description

Attention: Theory and Practice provides a balance between a readable overview of attention and an emphasis on how theories and paradigms for the study of attention have developed. The book highlights the important issues and major findings while giving sufficient details of experimental studies, models, and theories so that results and conclusions are easy to follow and evaluate. Rather than brushing over tricky technical details, the authors explain them clearly, giving readers the benefit of understanding the motivation for and techniques of the experiments in order to allow readers to think through results, models, and theories for themselves. Attention is an accessible text for advanced undergraduate and graduate students in psychology, as well as an important resource for researchers and practitioners interested in gaining an overview of the field of attention.