Modular Learning in Neural Networks
Modular Learning in Neural Networks

Author: Tomas Hrycej

Publisher: Wiley-Interscience

Published: 1992-10-09

Total Pages: 264

ISBN-13:

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"Modular Learning in Neural Networks covers the full range of conceivable approaches to the modularization of learning, including decomposition of learning into modules using supervised and unsupervised learning types; decomposition of the function to be mapped into linear and nonlinear parts; decomposition of the neural network to minimize harmful interferences between a large number of network parameters during learning; decomposition of the application task into subtasks that are learned separately; decomposition into a knowledge-based part and a learning part. The book attempts to show that modular learning based on these approaches is helpful in improving the learning performance of neural networks. It demonstrates this by applying modular methods to a pair of benchmark cases - a medical classification problem of realistic size, encompassing 7,200 cases of thyroid disorder; and a handwritten digits classification problem, involving several thousand cases. In so doing, the book shows that some of the proposed methods lead to substantial improvements in solution quality and learning speed, as well as enhanced robustness with regard to learning control parameters.".

Supervised Machine Learning for Text Analysis in R
Supervised Machine Learning for Text Analysis in R

Author: Emil Hvitfeldt

Publisher: CRC Press

Published: 2021-10-22

Total Pages: 402

ISBN-13: 1000461971

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Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.

Proceedings of 7th ASRES International Conference on Intelligent Technologies
Proceedings of 7th ASRES International Conference on Intelligent Technologies

Author: Karm Veer Arya

Publisher: Springer Nature

Published: 2023-07-05

Total Pages: 363

ISBN-13: 9819919126

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This book gathers the proceedings of the 7th International Conference on Intelligent Technologies (ICIT 2022) held on December 16-18, 2022, at the University of Pembangunan Jaya, Jakarta, Indonesia. The respective contributions from industrial practitioners and researchers present advanced studies related to application of intelligent technologies in various fields of research industry and society. This includes applications in variety of fields such as computational intelligence, data science and engineering, communication and networking, signal and image processing, electrical devices, circuits systems, robotics, instrumentation, automation, biomedical, and health care.

The 1997 IEEE International Conference on Neural Networks, June 9-12, 1997, Westin Galleria Hotel, Houston, Texas, USA.
The 1997 IEEE International Conference on Neural Networks, June 9-12, 1997, Westin Galleria Hotel, Houston, Texas, USA.

Author: IEEE Neural Networks Council

Publisher: Institute of Electrical & Electronics Engineers(IEEE)

Published: 1997

Total Pages: 684

ISBN-13:

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Instrumentation thrusts and achievements are reported in the field of simulation of aerospace dynamics. Quantified mapping techniques and measurements in research in unsteady fluid mechanics phenomena are described and the frontiers of speed and flight simulation are extended."

Ultimate Neural Network Programming with Python
Ultimate Neural Network Programming with Python

Author: Vishal Rajput

Publisher: Orange Education Pvt Ltd

Published: 2023-11-04

Total Pages: 366

ISBN-13: 9391246540

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Master Neural Networks for Building Modern AI Systems. KEY FEATURES ● Comprehensive Coverage of Foundational AI Concepts and Theories. ● In-Depth Exploration of Maths Behind Neural Network Mathematics. ● Effective Strategies for Structuring Deep Learning Code. ● Real-World Applications of AI Principles and Techniques. DESCRIPTION This book is a practical guide to the world of Artificial Intelligence (AI), unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI, demystifies complex AI math, teaches you to implement AI concepts, and explores high-level AI libraries. Throughout the chapters, readers are engaged with the book through practice exercises, and supplementary learnings. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles, letting readers focus on hands-on implementation or mathematical understanding. This book isn't just about using AI tools; it's a compass in the world of AI resources, empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration, experimentation, and proficiency in AI, equipping readers with the skills needed to excel in the AI industry. WHAT WILL YOU LEARN ● Leverage TensorFlow and Keras while building the foundation for creating AI pipelines. ● Explore advanced AI concepts, including dimensionality reduction, unsupervised learning, and optimization techniques. ● Master the intricacies of neural network construction from the ground up. ● Dive deeper into neural network development, covering derivatives, backpropagation, and optimization strategies. ● Harness the power of high-level AI libraries to develop production-ready code, allowing you to accelerate the development of AI applications. ● Stay up-to-date with the latest breakthroughs and advancements in the dynamic field of artificial intelligence. WHO IS THIS BOOK FOR? This book serves as an ideal guide for software engineers eager to explore AI, offering a detailed exploration and practical application of AI concepts using Python. AI researchers will find this book enlightening, providing clear insights into the mathematical concepts underlying AI algorithms and aiding in writing production-level code. This book is designed to enhance your skills and knowledge to create sophisticated, AI-powered solutions and advance in the multifaceted field of AI. TABLE OF CONTENTS 1. Understanding AI History 2. Setting up Python Workflow for AI Development 3. Python Libraries for Data Scientists 4. Foundational Concepts for Effective Neural Network Training 5. Dimensionality Reduction, Unsupervised Learning and Optimizations 6. Building Deep Neural Networks from Scratch 7. Derivatives, Backpropagation, and Optimizers 8. Understanding Convolution and CNN Architectures 9. Understanding the Basics of TensorFlow and Keras 10. Building End-to-end Image Segmentation Pipeline 11. Latest Advancements in AI Index

Large Language Models
Large Language Models

Author: Uday Kamath

Publisher: Springer Nature

Published: 2024

Total Pages: 496

ISBN-13: 3031656474

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Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.

Pythonic AI
Pythonic AI

Author: Arindam Banerjee

Publisher: BPB Publications

Published: 2023-10-31

Total Pages: 554

ISBN-13: 935551591X

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Unlock the power of AI with Python: Your Journey from Novice to Neural Nets KEY FEATURES ● Learn to code in Python and use Google Colab's hardware accelerators (GPU and TPU) to train and deploy AI models efficiently. ● Develop Convolutional Neural Networks (CNNs) using the TensorFlow 2 library for computer vision tasks. ● Develop sequence, attention-based, and Transformer models using the TensorFlow 2 library for Natural Language Processing (NLP) tasks. DESCRIPTION “Pythonic AI” is a book that teaches you how to build AI models using Python. It also includes practical projects in different domains so you can see how AI is used in the real world. Besides teaching how to build AI models, the book also teaches how to understand and explore the opportunities that AI presents. It includes several hands-on projects that walk you through successful AI applications, explaining concepts like neural networks, computer vision, natural language processing (NLP), and generative models. Each project in the book also reiterates and reinforces the important aspects of Python scripting. You'll learn Python coding and how it can be used to build cutting-edge AI applications. The author explains each essential line of Python code in detail, taking into account the importance and difficulty of understanding. By the end of the book, you will learn how to develop a portfolio of AI projects that will help you land your dream job in AI. WHAT YOU WILL LEARN ● Create neural network models using the TensorFlow 2 library. ● Develop Convolutional Neural Networks (CNNs) for computer vision tasks. ● Develop Sequence models for Natural Language Processing (NLP) tasks. ● Create Attention-based and Transformer models. ● Learn how to create Generative Adversarial Networks (GANs). WHO THIS BOOK IS FOR This book is for everyone who wants to learn how to build AI applications in Python, regardless of their experience level. Whether you're a student, a tech professional, a non-techie, or a technology enthusiast, this book will teach you the fundamentals of Python and AI, and show you how to apply them to real-world problems. TABLE OF CONTENTS 1. Python Kickstart: Concepts, Libraries, and Coding 2. Setting up AI Lab 3. Design My First Neural Network Model 4. Explore Designing CNN with TensorFlow 5. Develop CNN-based Image Classifier Apps 6. Train and Deploy Object Detection Models 7. Create a Text and Image Reader 8. Explore NLP for Advanced Text Analysis 9. Up and Running with Sequence Models 10. Using Sequence Models for Automated Text Classification 11. Create Attention and Transformer Models 12. Generating Captions for Images 13. Learn to Build GAN Models 14. Generate Artificial Faces Using GAN

Learn OpenAI Whisper
Learn OpenAI Whisper

Author: Josué R. Batista

Publisher: Packt Publishing Ltd

Published: 2024-05-31

Total Pages: 372

ISBN-13: 1835087493

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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.

How Smart Machines Think
How Smart Machines Think

Author: Sean Gerrish

Publisher: MIT Press

Published: 2018-10-30

Total Pages: 313

ISBN-13: 0262038404

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Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.