Deep Learning Convolutional Neural Networks Pemrograman Python Arduino Serta Aplikasi Pada Komputer Vision Speech Recognition Dan Mobile Robot
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Deep Learning Convolutional Neural Networks Pemrograman Python Arduino Serta Aplikasi Pada Komputer Vision Speech Recognition Dan Mobile Robot
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Author : Mada Sanjaya W. S., Ph.D.
language : id
Publisher: Bolabot
Release Date : 2024-06-30
Deep Learning Convolutional Neural Networks Pemrograman Python Arduino Serta Aplikasi Pada Komputer Vision Speech Recognition Dan Mobile Robot written by Mada Sanjaya W. S., Ph.D. and has been published by Bolabot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-30 with Computers categories.
Sebagai bagian dari praktisi, kami memahami pentingnya kebutuhan akan sumber daya yang dapat memberikan pandangan mendalam dan pemahaman yang komprehensif tentang teknologi mutakhir. Dalam buku ini, kami berusaha untuk memberikan landasan yang kokoh dan aplikasi praktis dalam penggunaan CNN, yang telah menjadi salah satu alat utama dalam riset dan pengembangan di berbagai bidang, termasuk Komputer Vision, Speech Recognition, dan Navigasi Mobile Robot. Setiap bab dalam buku ini disusun dengan literatur yang relevan dan contoh kode yang terperinci. Kami percaya bahwa buku ini dapat menjadi referensi yang berharga bagi para peneliti, mahasiswa, dan praktisi yang tertarik dalam memahami dan mengaplikasikan konsep Deep Learning dalam konteks praktis. Dalam proses penyusunan buku ini, kami ingin mengucapkan terima kasih kepada semua pihak yang telah memberikan dukungan, inspirasi, dan bimbingan. Semoga buku ini dapat memberikan kontribusi yang berarti dalam upaya Anda untuk mengembangkan pengetahuan dan keterampilan dalam bidang pemrograman Deep Learning.
Fisika Komputasi Berbasis Machine Learning Dengan Pemrograman Python
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Author : Mada Sanjaya W. S., Ph.D.
language : id
Publisher: Bolabot
Release Date : 2024-12-31
Fisika Komputasi Berbasis Machine Learning Dengan Pemrograman Python written by Mada Sanjaya W. S., Ph.D. and has been published by Bolabot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.
Buku ini dibagi menjadi beberapa bab yang mencakup dasar-dasar fisika komputasi, konsep machine learning, implementasi Python, hingga aplikasi nyata seperti simulasi gerak partikel, optimasi sistem fisika, dan prediksi berbasis data. Setiap bab disusun dengan pendekatan yang terstruktur dan disertai dengan contoh implementasi program Python agar pembaca dapat memahami konsep secara praktis dan aplikatif.
Theory Of Adoption
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Author : Durvasula Srirama Sastri
language : en
Publisher:
Release Date : 1909
Theory Of Adoption written by Durvasula Srirama Sastri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1909 with Adoption (Hindu law) categories.
Deep Learning With Applications Using Python
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Author : Navin Kumar Manaswi
language : en
Publisher: Apress
Release Date : 2018-04-04
Deep Learning With Applications Using Python written by Navin Kumar Manaswi and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-04 with Computers categories.
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Engage with chatbots using deep learning Who This Book Is For Data scientists and developers who want to adapt and build deep learning applications.
Power System Stability And Control
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Author : Leonard L. Grigsby
language : en
Publisher: CRC Press
Release Date : 2007-05-30
Power System Stability And Control written by Leonard L. Grigsby and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-30 with Science categories.
Updated with the latest developments and advances, the second edition of The Electric Power Engineering Handbook has grown so much that it is now presented as a set of five books. Now this authoritative coverage is available in easily digestible portions that are tightly focused and conveniently sized. Completing the set, Power System Stability and Control outlines the dynamics, operational aspects, and protection issues of power systems related to stability and control. In addition to updates and revisions throughout the chapters, it includes new sections in the areas of small signal stabilit.
Systematic Cause Analysis Technique Scat
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Author : International Loss Control Institute, Incorporated
language : en
Publisher:
Release Date :
Systematic Cause Analysis Technique Scat written by International Loss Control Institute, Incorporated and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Hands On Deep Learning Architectures With Python
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30
Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-30 with Computers categories.
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
Convolutional Neural Networks In Python
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Author : Anthony Williams
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-07-25
Convolutional Neural Networks In Python written by Anthony Williams and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-25 with categories.
Convolutional Neural Networks in Python (2nd Edition) Deep learning has been a great part of various scientific fields and since this is my third book regarding this topic, you already know the great significance of deep learning in comparison to traditional methods. At this point, you are also familiar with types of neural networks and their wide range of applications including image and speech recognition, natural language processing, video game development and other. On the other hand, this book is all about convolutional neural networks and how to use these neural networks in various tasks of automatic image and speech recognition in Python. You will also get a better insight into the architecture of convolutional layers as we are going deeper into this subject. Deep learning is pretty complex subject, but since you already have a fundamental knowledge of this topic, getting to know convolutional neural networks better is next logical step. What you will learn in Convolutional Neural Networks in Python: Architecture of convolutional neural networks Solving computer vision tasks using convolutional neural networks Python and computer vision Automatic image and speech recognition Theano and TenroeFlow image recognition How to use MNIST vision dataset What are commonly used convolutional filters Get this book today and learn more about Convolutional Neural Networks in Python!! PS: Get the Paperback and get this Ebook for FREE!!
Hands On Convolutional Neural Networks With Tensorflow
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Author : Iffat Zafar
language : en
Publisher: Packt Publishing
Release Date : 2018-08-28
Hands On Convolutional Neural Networks With Tensorflow written by Iffat Zafar and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-28 with Computers categories.
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
Practical Convolutional Neural Networks
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Author : Mohit Sewak
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-27
Practical Convolutional Neural Networks written by Mohit Sewak and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-27 with Computers categories.
One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learn From CNN basic building blocks to advanced concepts understand practical areas they can be applied to Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it Learn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more Understand the working of generative adversarial networks and how it can create new, unseen images Who this book is for This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.