Guide To Convolutional Neural Networks
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Convolutional Neural Networks In Python
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Author : Frank Millstein
language : en
Publisher: Frank Millstein
Release Date : 2020-07-06
Convolutional Neural Networks In Python written by Frank Millstein and has been published by Frank Millstein this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-06 with Computers categories.
Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book NOW and learn more about Convolutional Neural Networks in Python!
A Guide To Convolutional Neural Networks For Computer Vision
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Author : Salman Khan
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-02-13
A Guide To Convolutional Neural Networks For Computer Vision written by Salman Khan and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-13 with Computers categories.
Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
Guide To Convolutional Neural Networks
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Author : Hamed Habibi Aghdam
language : en
Publisher: Springer
Release Date : 2017-05-17
Guide To Convolutional Neural Networks written by Hamed Habibi Aghdam and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-17 with Computers categories.
This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.
Convolutional Neural Networks In Visual Computing
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Author : Ragav Venkatesan
language : en
Publisher: CRC Press
Release Date : 2017-10-23
Convolutional Neural Networks In Visual Computing written by Ragav Venkatesan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-23 with Computers categories.
This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.
Deep Learning S Dynamic Depths
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Author : KAIA. BARRETT
language : en
Publisher: Kaia Barrett
Release Date : 2025-02-10
Deep Learning S Dynamic Depths written by KAIA. BARRETT and has been published by Kaia Barrett this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-10 with Computers categories.
Dive headfirst into the fascinating world of Convolutional Neural Networks (CNNs) with Deep Learning's Dynamic Depths: A Comprehensive Guide to Convolutional Neural Networks This isn't just another tech book; it's your meticulously crafted roadmap through the intricacies of one of the most powerful tools in artificial intelligence today. Prepare to embark on a journey that will transform your understanding of how machines perceive and interpret visual data. We begin by laying a solid foundation in the first chapters, carefully dissecting what CNNs are and how their unique architectures differ from conventional neural networks. No prior knowledge is assumed; we walk you through the essential building blocks, ensuring you grasp the core concepts before advancing to more complex topics. You'll gain an intuitive understanding of how these networks are structured and why they are so exceptionally effective for image-related tasks. Next, prepare to get your hands dirty with a comprehensive look at convolutional layers. Understand the magic behind filters and kernels, how they extract features from images, and the effect of stride and padding on the overall process. Don't forget about pooling operations, the silent workhorses responsible for dimensionality reduction and feature invariance, which are covered in depth, leaving you with a robust comprehension of these fundamental components. Delve deeper into the core mechanisms by exploring the realm of activation functions. We'll untangle the complexities of ReLU and its variations, comparing and contrasting their strengths and weaknesses, allowing you to understand their critical role in neural network performance. We'll also examine the sigmoid and tanh functions and when to use them depending on the use case. The next chapters demystify the training process. Backpropagation is unveiled, and we'll guide you through how gradients are computed and used to adjust the network's internal weights. You'll become adept in the workings of various optimization algorithms, including Gradient Descent, Stochastic Gradient Descent, Adam, and RMSprop, learning about their strengths, limitations, and best use scenarios. Regularization methods and techniques to manage learning rate scheduling are also exposed, providing the knowledge to optimize your models and prevent the bane of any learning algorithm: overfitting. Whether you're an aspiring data scientist, a seasoned machine learning practitioner, or simply someone intrigued by the power of artificial intelligence, this book is your gateway to mastering CNNs. It's filled with clear explanations, illustrative examples, and a comprehensive view of the current state of CNNs. This is more than just a reference book; it's your comprehensive companion on your deep learning journey. From the fundamental building blocks to future trends, this guide is designed to empower you, transforming your understanding and allowing you to develop and apply this amazing technology. Uncover the dynamic depths of deep learning. Don't just follow the trend, shape it! Seize your knowledge key today, and ignite your CNN mastery!
Achievements And Trends In Material Forming
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Author : Gabriela Vincze
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2022-07-22
Achievements And Trends In Material Forming written by Gabriela Vincze and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-22 with Computers categories.
Peer-reviewed extended papers selected from the 25th International Conference on Material Forming (ESAFORM 2022) Peer-reviewed extended papers selected from the 25th International Conference on Material Forming (ESAFORM 2022), April 27-29, 2022, Portugal
Deep Learning
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Author : Shriram K Vasudevan
language : en
Publisher: CRC Press
Release Date : 2021-12-24
Deep Learning written by Shriram K Vasudevan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-24 with Computers categories.
Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.
Learn About Convolutional Neural Networks In Python With Data From The Mnist Dataset 1998
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Author : Feng Shi
language : en
Publisher:
Release Date : 2019
Learn About Convolutional Neural Networks In Python With Data From The Mnist Dataset 1998 written by Feng Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Neural networks (Computer science) categories.
This dataset is designed for teaching the convolutional neural network (CNN). The dataset is a subset of data derived from the 1998 MNIST dataset of handwritten digits, and the example demonstrates how to train the CNN to recognize handwritten digits in images. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Python.
Deep Learning With Python
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Author : Mark Graph
language : en
Publisher:
Release Date : 2019-10-15
Deep Learning With Python written by Mark Graph and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-15 with categories.
This book doesn't have any superpowers or magic formula to help you master the art of neural networks and deep learning. We believe that such learning is all in your heart. You need to learn a concept by heart and then brainstorm its different possibilities. I don't claim that after reading this book you will become an expert in Python and Deep Learning Neural Networks. Instead, you will, for sure, have a basic understanding of deep learning and its implications and real-life applications. Most of the time, what confuses us is the application of a certain thing in our lives. Once we know that, we can relate the subject to that particular thing and learn. An interesting thing is that neural networks also learn the same way. This makes it easier to learn about them when we know the basics. Let's take a look at what this book has to offer: ● The basics of Python including data types, operators and numbers. ● Advanced programming in Python with Python expressions, types and much more. ● A comprehensive overview of deep learning and its link to the smart systems that we are now building. ● An overview of how artificial neural networks work in real life. ● An overview of PyTorch. ● An overview of TensorFlow. ● An overview of Keras. ● How to create a convolutional neural network. ● A comprehensive understanding of deep learning applications and its ethical implications, including in the present and future. This book offers you the basic knowledge about Python and Deep Learning Neural Networks that you will need to lay the foundation for future studies. This book will start you on the road to mastering the art of deep learning neural networks. When I say that I don't have the magic formula to make you learn, I mean it. My point is that you should learn Python coding and Python libraries to build neural networks by practicing hard. The more you practice, the better it is for your skills. It is only after thorough and in depth practice that you will be able to create your own programs. Unlike other books, I don't claim that this book will make you a master of deep learning after a single read. That's not realistic, in fact, it's even a bit absurd. What I claim is that you will definitely learn about the basics. The rest is practice. The more you practice the better you code.
Hands On Deep Learning With Python
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Author : Rogers Isaacson
language : en
Publisher: Independently Published
Release Date : 2025-04-14
Hands On Deep Learning With Python written by Rogers Isaacson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Computers categories.
Unlock the world of deep learning with Hands-On Deep Learning with Python. This in-depth guide will teach you how to build, train, and optimize neural networks, convolutional networks, and recurrent networks for real-world applications using Python. Whether you're a beginner looking to break into the world of AI or an experienced developer seeking to deepen your knowledge of deep learning techniques, this book provides step-by-step instructions and practical examples to help you implement cutting-edge models. Python, along with powerful libraries like TensorFlow, Keras, and PyTorch, is the most popular ecosystem for deep learning. This book will show you how to use these libraries to build state-of-the-art neural networks for a wide range of applications, from image classification and object detection to natural language processing and time-series forecasting. Inside, you'll learn: The fundamentals of deep learning, including what neural networks are, how they work, and the different types of networks (e.g., feedforward, convolutional, and recurrent) How to set up and use popular Python libraries for deep learning, such as TensorFlow, Keras, and PyTorch The principles behind training neural networks, including backpropagation, optimization algorithms, and loss functions How to build and train Convolutional Neural Networks (CNNs) for image recognition, classification, and segmentation tasks The basics of Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) for sequential data like text, speech, and time-series forecasting Advanced deep learning techniques, including transfer learning, data augmentation, and hyperparameter tuning How to evaluate model performance using metrics such as accuracy, precision, recall, and confusion matrices How to deploy deep learning models into production for real-time use Real-world case studies and projects that help you apply deep learning to various domains like healthcare, finance, and entertainment By the end of this book, you'll have the skills to implement advanced deep learning models using Python and apply them to solve practical problems. Hands-On Deep Learning with Python will empower you to tackle challenges in AI and machine learning and start building your own deep learning applications. Key Features: Step-by-step guidance for building neural networks, CNNs, and RNNs Hands-on projects using real-world datasets to practice and reinforce your learning Learn to implement deep learning techniques using Python libraries like TensorFlow, Keras, and PyTorch Advanced deep learning techniques like transfer learning, hyperparameter tuning, and model evaluation Practical advice for deploying deep learning models into real-world applications Start your deep learning journey today with Hands-On Deep Learning with Python and learn how to build, train, and deploy state-of-the-art neural networks for real-world problems.