Generative Ai In Action
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| Author | : Vladimir Bok |
| Publisher | : Simon and Schuster |
| Total Pages | : 367 |
| Release | : 2019-09-09 |
| Genre | : Computers |
| ISBN | : 1638354235 |
Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks' one that generates content and the other that rejects samples that are of poor quality. GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
| Author | : Ben Wilson |
| Publisher | : Simon and Schuster |
| Total Pages | : 879 |
| Release | : 2022-05-17 |
| Genre | : Computers |
| ISBN | : 1638356580 |
Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.
| Author | : Andrew Burgess |
| Publisher | : Springer |
| Total Pages | : 187 |
| Release | : 2017-11-15 |
| Genre | : Business & Economics |
| ISBN | : 3319638203 |
This book takes a pragmatic and hype–free approach to explaining artificial intelligence and how it can be utilised by businesses today. At the core of the book is a framework, developed by the author, which describes in non–technical language the eight core capabilities of Artificial Intelligence (AI). Each of these capabilities, ranging from image recognition, through natural language processing, to prediction, is explained using real–life examples and how they can be applied in a business environment. It will include interviews with executives who have successfully implemented AI as well as CEOs from AI vendors and consultancies. AI is one of the most talked about technologies in business today. It has the ability to deliver step–change benefits to organisations and enables forward–thinking CEOs to rethink their business models or create completely new businesses. But most of the real value of AI is hidden behind marketing hyperbole, confusing terminology, inflated expectations and dire warnings of ‘robot overlords’. Any business executive that wants to know how to exploit AI in their business today is left confused and frustrated. As an advisor in Artificial Intelligence, Andrew Burgess regularly comes face–to–face with business executives who are struggling to cut through the hype that surrounds AI. The knowledge and experience he has gained in advising them, as well as working as a strategic advisor to AI vendors and consultancies, has provided him with the skills to help business executives understand what AI is and how they can exploit its many benefits. Through the distilled knowledge included in this book business leaders will be able to take full advantage of this most disruptive of technologies and create substantial competitive advantage for their companies.
| Author | : Adam Bohr |
| Publisher | : Academic Press |
| Total Pages | : 385 |
| Release | : 2020-06-21 |
| Genre | : Computers |
| ISBN | : 0128184396 |
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
| Author | : Wee Hyong Tok |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 193 |
| Release | : 2021-09-30 |
| Genre | : Computers |
| ISBN | : 1492077038 |
Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling
| Author | : Mark Enser |
| Publisher | : John Catt |
| Total Pages | : 117 |
| Release | : 2020-09-18 |
| Genre | : Education |
| ISBN | : 1913808300 |
Generative Learning in Action helps to answer the question: which activities can students carry out to create meaningful learning? It does this by considering how we, as teachers, can implement the eight strategies for generative learning set out in the work of Fiorella and Mayer in their seminal 2015 work Learning as a Generative Activity: Eight Learning Strategies that Promote Learning. At a time when a great deal of attention has been paid to the teaching and learning from the perspective of effective instruction, Generative Learning looks at the flip side of coin and considers what is happening in the minds of the learner. This book takes a teachers-eye view of a range of theories of learning and keeps their application to the classroom firmly in mind through the use of case studies and reference to day to day practice. Generative Learning in Action also discusses the key considerations and potential limitations of each of the strategies, as well as how you could implement these in your own practice and more widely across a school. The authors bring a wealth of experience to this topic. Zoe Enser was a classroom English teacher for over 20 years as well as head of department and school leader in charge of improving teaching and learning. She is now lead specialist advisor for Kent with The Education People. Mark Enser has been a geography teacher for the best part of two decades as well as a head of department and research lead. He is the author of Making Every Geography Lesson Count and Teach Like Nobody's Watching as well as a TES columnist.
| Author | : Mark Treveil |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 171 |
| Release | : 2020-11-30 |
| Genre | : Computers |
| ISBN | : 1098116429 |
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
| Author | : Rodolfo E. Biasca |
| Publisher | : Outskirts Press |
| Total Pages | : 488 |
| Release | : 2024-09-20 |
| Genre | : Business & Economics |
| ISBN | : 1977278329 |
R. E. Biasca has been a leading international business consultant and educator for nearly sixteen decades. He has written fifteen books in Spanish, and for the first time, Renewal: An Effective Transformative Change Framework brings his extensive knowledge to the English-speaking world. Biasca’s Model has come to be seen by many as a practical guide to business transformation. Using a medical analogy, the model guides company leadership from diagnosis of their organization’s current situation through a focus on preparing for the next one: 1. Analysis (diagnosis and prognosis) 2. Innovation (prescription) 3. Execution (therapy) 4. Consolidation (preventive medicine) Holistic and interdisciplinary, immune to passing trends yet flexible enough to grow from practitioner feedback, Biasca’s Model is perfect for CEOs, board members, professors, and students in executive education and MBA programs.
| Author | : Kelly Goss |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 706 |
| Release | : 2023-08-25 |
| Genre | : Computers |
| ISBN | : 1803246626 |
Strategize and create automated business workflows with Zapier, including AI-integrated functionalities such as the ChatGPT plugin and the OpenAI integration, to minimize repetitive tasks without using code Key Features Discover the newest Zapier features including OpenAI integration and the ChatGPT plugin Explore expert tips and real-life examples to connect 6000+ business apps and automate tasks with Zapier Learn how to manage your account effectively and troubleshoot problems with your Zaps Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionOrganizations experience significant issues with productivity when dealing with manual and repetitive tasks. Automate it with Zapier and Generative AI, second edition has been extensively revised to help you analyze your processes and identify repetitive tasks that can be automated between 6000+ cloud-based business applications. This book includes all Zapier’s newest features such as AI functionality using the ChatGPT plugin, drafts, reordering and duplicating steps and paths, subfolders and version history, as well as built-in apps such as Looping, Sub-Zap, Interfaces, Tables, and Transfer. The chapters also contain examples covering various use cases sourced from the Zapier user community. You'll learn how to implement automation in your organization along with key principles and terminology, and take the first steps toward using Zapier. As you advance, you'll learn how to use Zapier’s native functionality and all 27 built-in apps such as Filter, Paths, Formatter, Digest, and Scheduler to enable you to build multi-step Zaps. You’ll also discover how to manage your Zapier account effectively, as well as how to troubleshoot technical problems with your workflows, and use the OpenAI integration to automate AI tasks. By the end of this book, you'll be able to automate your manual and repetitive tasks using Zapier.What you will learn Think outside the box to simplify business workflows and solve productivity problems Strategize how to optimally structure and build your workflow automation in Zapier to prevent errors and excessive task usage Explore the latest built-in apps including Transfer, Interfaces, Tables, Looping, Sub-Zap, and the ChatGPT plugin Discover how to use AI-integrated apps and features with automation Create complex multi-step Zaps using logic, formatting, and calculations Effectively manage your account and troubleshoot problems with your Zaps Who this book is forThis book is for business owners, operations managers, and teams in micro, small, or medium-sized businesses looking at automating repetitive tasks and increasing their productivity using Zapier and AI-integrated features. Service providers offering digital process improvement, systemization, and automation services to their clients such as solutions architects, process consultants, business analysts, virtual assistants, CRM consultants, OBMs, bookkeepers and accountants will find this book extremely useful. Suitable for new and experienced Zapier users.
| Author | : David Foster |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 301 |
| Release | : 2019-06-28 |
| Genre | : Computers |
| ISBN | : 1492041890 |
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
