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Text Generative Ai Courseware
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Book Synopsis Text Generative AI courseware by : Wahbe Rezek
Download or read book Text Generative AI courseware written by Wahbe Rezek and published by Van Haren. This book was released on with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: The course is mainly practical (applying generative AI/prompt-engineering), in action activities where you make prompts and learn best practices on Text Generative AI, while doing. Gain a comprehensive understanding of Text Generative AI and its capabilities Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases Understanding of the architecture behind the model Master the skill of prompt engineering for effective interaction with Text Generative AI Learn how to integrate Text Generative AI into existing workflows Understand the limitations and risks associated with Text Generative AI Individuals who need a basic understanding Text Generative AI Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with it Business Analyst. The course is mainly practical, with interactive activities where you make prompts and learn by doing. You will also get feedback and guidance from experienced instructors and peers. By the end of the course, you will be able to: • Gain a comprehensive understanding of Text Generative AI and its capabilities • Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases • Master the skill of prompt engineering for effective interaction with Text Generative AI • Learn how to integrate Text Generative AI into existing workflows • Understand the limitations and risks associated with Text Generative AI The course is suitable for anyone who needs a basic understanding of Text Generative AI, such as: • Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with them • Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI • Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field.
Book Synopsis Text Generative AI Courseware by : Van Haren Publishing
Download or read book Text Generative AI Courseware written by Van Haren Publishing and published by . This book was released on 2024-05-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: ''The course is mainly practical (applying generative AI/prompt-engineering), in action activities where you make prompts and learn best practices on Text Generative AI, while doing. Gain a comprehensive understanding of Text Generative AI and its capabilities Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases Understanding of the architecture behind the model Master the skill of prompt engineering for effective interaction with Text Generative AI Learn how to integrate Text Generative AI into existing workflows Understand the limitations and risks associated with Text Generative AI Individuals who need a basic understanding Text Generative AI Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with it Business Analyst. The course is mainly practical, with interactive activities where you make prompts and learn by doing. You will also get feedback and guidance from experienced instructors and peers. By the end of the course, you will be able to: *Gain a comprehensive understanding of Text Generative AI and its capabilities *Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases *Master the skill of prompt engineering for effective interaction with Text Generative AI *Learn how to integrate Text Generative AI into existing workflows *Understand the limitations and risks associated with Text Generative AI The course is suitable for anyone who needs a basic understanding of Text Generative AI, such as: *Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with them *Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI *Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field. ced instructors and peers. By the end of the course, you will be able to: *Gain a comprehensive understanding of Text Generative AI and its capabilities *Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases *Master the skill of prompt engineering for effective interaction with Text Generative AI *Learn how to integrate Text Generative AI into existing workflows *Understand the limitations and risks associated with Text Generative AI The course is suitable for anyone who needs a basic understanding of Text Generative AI, such as: *Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with them *Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI *Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field. ied (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field.ced instructors and peers. By the end of the course, you will be able to: *Gain a comprehensive understanding of Text Generative AI and its capabilities *Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases *Master the skill of prompt engineering for effective interaction with Text Generative AI *Learn how to integrate Text Generative AI into existing workflows *Understand the limitations and risks associated with Text Generative AI The course is suitable for anyone who needs a basic understanding of Text Generative AI, such as: *Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with them *Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI *Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field. ced instructors and peers. By the end of the course, you will be able to: *Gain a comprehensive understanding of Text Generative AI and its capabilities *Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases *Master the skill of prompt engineering for effective interaction with Text Generative AI *Learn how to integrate Text Generative AI into existing workflows *Understand the limitations and risks associated with Text Generative AI The course is suitable for anyone who needs a basic understanding of Text Generative AI, such as: *Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with them *Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI *Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field. ied (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field.ied (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field.ced instructors and peers. By the end of the course, you will be able to: *Gain a comprehensive understanding of Text Generative AI and its capabilities *Develop critical thinking skills to evaluate the potential of Text Generative AI in different business use cases *Master the skill of prompt engineering for effective interaction with Text Generative AI *Learn how to integrate Text Generative AI into existing workflows *Understand the limitations and risks associated with Text Generative AI The course is suitable for anyone who needs a basic understanding of Text Generative AI, such as: *Professionals who want to learn the potential and pitfalls of Text Generative AI and how to deal with them *Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI *Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field. ied (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field.usiness analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Text Generative AI *Students who want to explore the field of Text Generative AI and its applications The course prepares you for the EDF Certified (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field. ied (Text) Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Text Generative AI Ambassador and gain a competitive edge in your field.
Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Book Synopsis Artificial Intelligence with Python by : Prateek Joshi
Download or read book Artificial Intelligence with Python written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2017-01-27 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Book Synopsis AI Fundamentals Courseware by : Reinier van den Biggelaar
Download or read book AI Fundamentals Courseware written by Reinier van den Biggelaar and published by Van Haren. This book was released on with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis AI and Machine Learning for Coders by : Laurence Moroney
Download or read book AI and Machine Learning for Coders written by Laurence Moroney and published by O'Reilly Media. This book was released on 2020-10-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving
Book Synopsis Generative Intelligence and Intelligent Tutoring Systems by : Angelo Sifaleras
Download or read book Generative Intelligence and Intelligent Tutoring Systems written by Angelo Sifaleras and published by Springer Nature. This book was released on with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A First Course in Artificial Intelligence by : Osondu Oguike
Download or read book A First Course in Artificial Intelligence written by Osondu Oguike and published by Bentham Science Publishers. This book was released on 2021-07-14 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: The importance of Artificial Intelligence cannot be over-emphasised in current times, where automation is already an integral part of industrial and business processes. A First Course in Artificial Intelligence is a comprehensive textbook for beginners which covers all the fundamentals of Artificial Intelligence. Seven chapters (divided into thirty-three units) introduce the student to key concepts of the discipline in simple language, including expert system, natural language processing, machine learning, machine learning applications, sensory perceptions (computer vision, tactile perception) and robotics. Each chapter provides information in separate units about relevant history, applications, algorithm and programming with relevant case studies and examples. The simplified approach to the subject enables beginners in computer science who have a basic knowledge of Java programming to easily understand the contents. The text also introduces Python programming language basics, with demonstrations of natural language processing. It also introduces readers to the Waikato Environment for Knowledge Analysis (WEKA), as a tool for machine learning. The book is suitable for students and teachers involved in introductory courses in undergraduate and diploma level courses which have appropriate modules on artificial intelligence.
Book Synopsis Computational Linguistics and Intelligent Text Processing by : Alexander Gelbukh
Download or read book Computational Linguistics and Intelligent Text Processing written by Alexander Gelbukh and published by Springer Science & Business Media. This book was released on 2005-01-31 with total page 845 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2005, held in Mexico City, Mexico in February 2005. The 53 revised full papers and 35 revised short papers presented together with 4 invited papers were carefully reviewed and selected from 151 submissions. The papers are organized in topical sections on computational linguistics forum; semantics and discourse; parsing and syntactic disambiguation; morphology; anaphora and conference; word sense disambiguation; lexical resources; natural language generation; machine translation; speech and natural language interfaces; language documentation; information extraction, information retrieval; question answering; summarization; text classification, categorization, and clustering; named entity recognition; language identification; and spelling and style checking.
Book Synopsis ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning by :
Download or read book ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning written by and published by STAR SCHOLARS PRESS. This book was released on 2024-04-03 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning
Book Synopsis The Impact of Artificial Intelligence (AI) on Global Higher Education: Opportunities and Challenges of Using ChatGPT and Generative AI by :
Download or read book The Impact of Artificial Intelligence (AI) on Global Higher Education: Opportunities and Challenges of Using ChatGPT and Generative AI written by and published by STAR SCHOLARS PRESS. This book was released on with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Impact of Artificial Intelligence (AI) on Global Higher Education: Opportunities and Challenges of Using ChatGPT and Generative AI
Book Synopsis Generative Artificial Intelligence by : Shivam Solanki
Download or read book Generative Artificial Intelligence written by Shivam Solanki and published by Apress. This book was released on 2024-10-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today’s AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will Learn What is Generative Artificial Intelligence? What are text-to-image synthesis techniques and conditional image generation? What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques? What are text-to-video models and how do you tune them? What are large language models, and how do you tune them? Who This Book Is For Those with intermediate to advanced technical knowledge in artificial intelligence and machine learning
Book Synopsis Competing in the Age of AI by : Marco Iansiti
Download or read book Competing in the Age of AI written by Marco Iansiti and published by Harvard Business Press. This book was released on 2020-01-07 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: "a provocative new book" — The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI.
Book Synopsis Deep Learning with PyTorch by : Luca Pietro Giovanni Antiga
Download or read book Deep Learning with PyTorch written by Luca Pietro Giovanni Antiga and published by Simon and Schuster. This book was released on 2020-07-01 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: “We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Book Synopsis Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation by : Sharma, Ramesh C.
Download or read book Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation written by Sharma, Ramesh C. and published by IGI Global. This book was released on 2024-02-07 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rise of generative Artificial Intelligence (AI) signifies a momentous stride in the evolution of Large Language Models (LLMs) within the expansive sphere of Natural Language Processing (NLP). This groundbreaking advancement ripples through numerous facets of our existence, with education, AI literacy, and curriculum enhancement emerging as focal points of transformation. Within the pages of Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, readers embark on a journey into the heart of this transformative phenomenon. Generative AI's influence extends deeply into education, touching the lives of educators, administrators, policymakers, and learners alike. Within the pages of this book, we explore the intricate art of prompt engineering, a skill that shapes the quality of AI-generated educational content. As generative AI becomes increasingly accessible, this comprehensive volume empowers its audience, by providing them with the knowledge needed to navigate and harness the potential of this powerful tool.
Book Synopsis Artificial Intelligence Foundations by : Doug Rose
Download or read book Artificial Intelligence Foundations written by Doug Rose and published by Independently Published. This book was released on 2019-03-31 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: This tutorial provides introductory knowledge on Artificial Intelligence. It would come to a great help if you are about to select Artificial Intelligence as a course subject. You can briefly know about the areas of AI in which research is prospering.This tutorial is prepared for the students at beginner level who aspire to learn Artificial Intelligence.
Book Synopsis Deep Learning with Structured Data by : Mark Ryan
Download or read book Deep Learning with Structured Data written by Mark Ryan and published by Simon and Schuster. This book was released on 2020-12-08 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
