Learn Openai Whisper
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Learn Openai Whisper
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Author : Josué R. Batista
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31
Learn Openai Whisper written by Josué R. Batista 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 2024-05-31 with Computers categories.
Master automatic speech recognition (ASR) with groundbreaking generative AI for unrivaled accuracy and versatility in audio processing Key Features Uncover the intricate architecture and mechanics behind Whisper's robust speech recognition Apply Whisper's technology in innovative projects, from audio transcription to voice synthesis Navigate the practical use of Whisper in real-world scenarios for achieving dynamic tech solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAs the field of generative AI evolves, so does the demand for intelligent systems that can understand human speech. Navigating the complexities of automatic speech recognition (ASR) technology is a significant challenge for many professionals. This book offers a comprehensive solution that guides you through OpenAI's advanced ASR system. You’ll begin your journey with Whisper's foundational concepts, gradually progressing to its sophisticated functionalities. Next, you’ll explore the transformer model, understand its multilingual capabilities, and grasp training techniques using weak supervision. The book helps you customize Whisper for different contexts and optimize its performance for specific needs. You’ll also focus on the vast potential of Whisper in real-world scenarios, including its transcription services, voice-based search, and the ability to enhance customer engagement. Advanced chapters delve into voice synthesis and diarization while addressing ethical considerations. By the end of this book, you'll have an understanding of ASR technology and have the skills to implement Whisper. Moreover, Python coding examples will equip you to apply ASR technologies in your projects as well as prepare you to tackle challenges and seize opportunities in the rapidly evolving world of voice recognition and processing.What you will learn Integrate Whisper into voice assistants and chatbots Use Whisper for efficient, accurate transcription services Understand Whisper's transformer model structure and nuances Fine-tune Whisper for specific language requirements globally Implement Whisper in real-time translation scenarios Explore voice synthesis capabilities using Whisper's robust tech Execute voice diarization with Whisper and NVIDIA's NeMo Navigate ethical considerations in advanced voice technology Who this book is for Learn OpenAI Whisper is designed for a diverse audience, including AI engineers, tech professionals, and students. It's ideal for those with a basic understanding of machine learning and Python programming, and an interest in voice technology, from developers integrating ASR in applications to researchers exploring the cutting-edge possibilities in artificial intelligence.
Computing And Machine Learning
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Author : Jagdish Chand Bansal
language : en
Publisher: Springer Nature
Release Date : 2024-12-24
Computing And Machine Learning written by Jagdish Chand Bansal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-24 with Computers categories.
This book features high-quality research papers presented at the International Conference on Computing and Machine Learning (CML 2024), organized by Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India during April 29–30, 2024. The book presents diverse range of topics, including machine learning algorithms and models, deep learning and neural networks, computer vision and image processing, natural language processing, robotics and automation, reinforcement learning, big data analytics, cloud computing, internet of things, human-robot interaction, ethical and social implications of AI, applications in healthcare, finance, and industry, computer modeling, quantum computing, high-performance computing, cognitive and parallel computing, cloud computing, distributed computing, embedded computing, human-centered computing and mobile computing.
Deep Learning With Jax
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Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-10-29
Deep Learning With Jax written by Grigory Sapunov and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Computers categories.
"The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google's Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. Deep learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX's concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You'll learn how to use JAX's ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment" --Publisher's description.
Learning And Collaboration Technologies
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Author : Brian K. Smith
language : en
Publisher: Springer Nature
Release Date : 2025-05-30
Learning And Collaboration Technologies written by Brian K. Smith and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-30 with Computers categories.
The three-volume set LNCS 15806–15808 constitutes the thoroughly refereed proceedings of the 12th International Conference on Learning and Collaboration Technologies, LCT 2025, held as part of the 27th International Conference, HCI International 2025, which took place in Gothenburg, Sweden, June 22-17, 2025. The total of 1430 papers and 355 posters included in the HCII 2025 proceedings was carefully reviewed and selected from 7972 submissions. The papers have been organized in topical sections as follows: Part I: Designing Learning Experiences; Technological Innovation in Education Part II: From Human Teachers to AI Educators; Intelligent Learning Environments Part III: Serious Games and Gamification; Immersive Learning; Understanding Learning Experiences
Data Labeling In Machine Learning With Python
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Author : Vijaya Kumar Suda
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31
Data Labeling In Machine Learning With Python written by Vijaya Kumar Suda 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 2024-01-31 with Computers categories.
Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling Key Features Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.
Deep Learning Theory And Applications
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Author : Ana Fred
language : en
Publisher: Springer Nature
Release Date : 2024-08-20
Deep Learning Theory And Applications written by Ana Fred and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-20 with Computers categories.
The two-volume set CCIS 2171 and 2172 constitutes the refereed papers from the 5th INternational Conference on Deep Learning Theory and Applications, DeLTA 2024, which took place in Dijon, France, during July 10-11, 2024. The 44 papers included in these proceedings were carefully reviewed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial intelligence, etc.
Applying Machine Learning In Science Education Research
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Author : Peter Wulff
language : en
Publisher: Springer Nature
Release Date : 2025-02-28
Applying Machine Learning In Science Education Research written by Peter Wulff and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-28 with Science categories.
This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects. These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research. ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context. The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research. It then presents exemplar case studies of ML uses in both formal and informal science education settings. These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case. The textbook concludes with a discussion of opportunities and potential future directions for ML in science education. This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.
Natural Language Processing Exam Prep
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date : 2024-10-26
Natural Language Processing Exam Prep written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Study Aids categories.
Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
Practical Machine Learning From Pictures To The Cloud 2025
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Author : AUTHOR:1-Praneet Amul Akash Cherukuri AUTHOR:2-Dr. Santosh Kumar Henge
language : en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date :
Practical Machine Learning From Pictures To The Cloud 2025 written by AUTHOR:1-Praneet Amul Akash Cherukuri AUTHOR:2-Dr. Santosh Kumar Henge and has been published by RAVEENA PRAKASHAN OPC PVT LTD this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
PREFACE The past decade has moved machine learning from academic curiosity to an invisible utility pulsing through every photograph we snap and every swipe we make. A face unlocks a phone, a drone inspects a bridge, a doctor consults an algorithm before a diagnosis—all powered by models that see, learn, and act in real time. Yet for students and engineers stepping into the field, the journey from inquisitive “Hello-world” notebook to a production-grade model running on an edge device or a cloud endpoint can feel disjointed and opaque. Practical Machine Learning: From Pictures to the Cloud was born of that gap. In our classrooms and industry collaborations at S R University, we watched learners master isolated concepts—convolutional layers, hyper-parameter tuning, REST APIs—without a blueprint that tied them together. This book offers that blueprint. We start with raw pixels, guide you through feature engineering and modern deep-learning architectures, and then scale the conversation outward: how to train responsibly, deploy at cloud scale, monitor for drift, and govern for fairness and privacy. What makes the text “practical” is its bias toward end-to-end reproducibility. Every chapter couple’s theory with hands-on labs drawn from real engagements in health care, smart cities, retail, and autonomous systems. Code examples ship as containerised notebooks; pipeline diagrams map directly to the managed services of AWS, Google Cloud, Azure, and open-source stacks like Kubeflow and Feast. Whether your workstation is a laptop or a GPU cluster, you can follow the same lifecycle we use in production. Equally vital is the ethical lens threaded throughout. As image models grow more capable, they also magnify risks—bias, surveillance, ecological cost. You will find checklists, case studies, and policy references alongside optimisation tricks, because robustness and responsibility are no longer optional extras; they are success criteria. The book is organised in three movements: 1. Seeing – fundamentals of image data, classical vision, and modern convolutional/transformer networks. 2. Learning – advanced training techniques, transfer learning, hyper-parameter tuning, and explainability. 3. Serving – scalable pipelines, cloud deployment, edge inference, monitoring, cost governance, and compliance. Our intended audience spans senior undergraduates, graduate students, and practitioners who know basic Python and linear algebra but want to take the leap into full-stack machine-learning engineering. We owe gratitude to our students, whose incisive questions shaped the narrative, and to industry partners who opened their architectures for case studies. Any errors that remain are ours alone. We hope this book becomes your desk companion as you turn pixels into insights and models into value—responsibly, reproducibly, and at scale. Authors
Ultimate Deepfake Detection Using Python Master Deep Learning Techniques Like Cnns Gans And Transformers To Detect Deepfakes In Images Audio And Videos Using Python
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Author : Dr. Nimrita
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-09-21
Ultimate Deepfake Detection Using Python Master Deep Learning Techniques Like Cnns Gans And Transformers To Detect Deepfakes In Images Audio And Videos Using Python written by Dr. Nimrita and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-21 with Computers categories.
Deepfake Detection Unlocked: Python Approaches for Deepfake Images, Videos, Audio Detection. Key Features● Comprehensive and graded approach to Deepfake detection using Python and its libraries. ● Practical implementation of deepfake detection techniques using Python. ● Hands-on chapters for detecting deepfake images, videos, and audio. ● Covers Case study for providing real-world application of deepfake detection. Book DescriptionIn today's digital world, mastering deepfake detection is crucial, with deepfake content increasing by 900% since 2019 and 96% used for malicious purposes like fraud and disinformation. "Ultimate Deepfake Detection with Python" equips you with the skills to combat this threat using Python’s AI libraries, offering practical tools to protect digital security across images, videos, and audio. This book explores generative AI and deepfakes, giving readers a clear understanding of how these technologies work and the challenges of detecting them. With practical Python code examples, it provides the tools necessary for effective deepfake detection across media types like images, videos, and audio. Each chapter covers vital topics, from setting up Python environments to using key datasets and advanced deep learning techniques. Perfect for researchers, developers, and cybersecurity professionals, this book enhances technical skills and deepens awareness of the ethical issues around deepfakes. Whether building new detection systems or improving current ones, this book offers expert strategies to stay ahead in digital media security. What you will learn ● Understand the fundamentals of generative AI and deepfake technology and the potential risks they pose. ● Explore the various methods and techniques used to identify deepfakes, as well as the obstacles faced in this field. ● Learn to use essential datasets and label image, video, and audio data for building deepfake detection models ● Apply advanced machine learning models like CNNs, RNNs, GANs, and Transformers for deepfake detection ● Master active and passive methods for detecting face manipulation and build CNN-based image detection systems ● Detect manipulations in videos, develop a detection system, and evaluate its performance using key metrics ● Build and implement a practical deepfake detection system to understand how these techniques are applied in real-world scenarios. Table of Contents1. Introduction to Generative AI and Deepfake Technology 2. Deepfake Detection Principles and Challenges 3. Ethical Considerations with the Use of Deepfakes 4. Setting Up your Machine for Deepfake Detection using Python 5. Deepfake Datasets 6. Techniques for Deepfake Detection 7. Detection of Deepfake Images 8. Detection of Deepfake Video 9. Detection of Deepfake Audio 10. Case Study in Deepfake Detection Index