Text Generative AI courseware
Text Generative AI courseware

Author: Fabienne Mouris

Publisher: Van Haren

Published: 2023-10-20

Total Pages: 105

ISBN-13: 9401810869

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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.

Artificial Intelligence with Python
Artificial Intelligence with Python

Author: Prateek Joshi

Publisher: Packt Publishing Ltd

Published: 2017-01-27

Total Pages: 437

ISBN-13: 1786469677

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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.

Deep Learning for Coders with fastai and PyTorch
Deep Learning for Coders with fastai and PyTorch

Author: Jeremy Howard

Publisher: O'Reilly Media

Published: 2020-06-29

Total Pages: 624

ISBN-13: 1492045497

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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

AI Fundamentals Courseware
AI Fundamentals Courseware

Author: Reinier van den Biggelaar

Publisher: Van Haren

Published: 2023-09-26

Total Pages: 445

ISBN-13: 9401810583

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The AI Fundamentals courseware offers an AI training course designed for professionals in business or government environments who want to understand the benefits and applications of AI in their work environment. This course covers topics such as data management for AI, building and assessing AI applications, ethics and trustworthiness, and organizational success factors for enabling humans and machines to work together. The course addresses key questions such as “Where does Data Management end and AI application begin?” from a management perspective. Subjects covered include the applications and benefits of AI, data and robots, predictions and algorithms, machine and deep learning, building and reviewing AI applications, data management for AI, ethics and trustworthiness, organizational success factors for helping humans and machines work together, and the future of AI. This courseware educates for three certifications within it’s three-day combined program. It’s also possible to cut the material in pieces for a module teaching approach. The EXIN BCS Artificial Intelligence Essentials, testing the fundamental concepts of AI. This AI for Business and Government certification (the AI Brevet) which was established by the Netherlands AI Coalition (NL AIC) as a standard for professionals who want to use Artificial Intelligence. EXIN BCS Artificial Intelligence Foundation, which has a more IT-technical perspective.

THE EVOLUTION OF CHATGPT: FROM TEXT TO VOICE IN VIRTUAL WORDS
THE EVOLUTION OF CHATGPT: FROM TEXT TO VOICE IN VIRTUAL WORDS

Author: Virendra Pal Singh

Publisher: Xoffencerpublication

Published: 2023-12-12

Total Pages: 175

ISBN-13: 8119534859

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ChatGPT is an artificial intelligence (AI)-based chatbot that makes use of natural language processing in order to provide conversational discourse that sounds and feels like it was done with a real person. This is accomplished by producing conversational discourse in many languages, including English, Japanese, and Korean. ChatGPT is a cloud-based application that may be accessed using the Azure cloud platform. It was created by Microsoft Research. The language model is able to provide responses to inquiries as well as generate a variety of written material, such as emails, posts on social networking sites, and posts on platforms that enable users to write in the format of an essay. On November 30, 2022, OpenAI developed ChatGPT, a big language model-based chatbot, and made it available to the public. Only the beta version of ChatGPT was first made accessible to users. Through the utilization of this feature, users are provided with the capacity to enhance and lead a discussion in the direction of a desired duration, structure, style, level of information, and language. This ability is made available to users. What we refer to as the Chat Generative Pre-trained Transformer is referred to as ChatGPT in its shorter version. In order to determine the context of the dialogue at each level of the discussion, a method known as "prompt engineering" is utilized. This method involves looking at the prompts and responses that came before it in the conversation. ChatGPT is built on either the GPT-3.5 or the GPT-4 model, both of which are a part of OpenAI's proprietary line of generative pre-trained transformer (GPT) models. Both models were used in the construction of ChatGPT. Conversational interactions are one of the primary focuses of ChatGPT's design. The Google-developed transformer architecture serves as the foundation for these models, which have been modified for use in conversational applications by employing a mix of supervised learning and reinforcement learning strategies. These methods were utilized in order to get a higher level of precision with the models. The research preview version of ChatGPT was made accessible to the general public for the very first time as a free service.

Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning Illustrated
Deep Learning Illustrated

Author: Jon Krohn

Publisher: Addison-Wesley Professional

Published: 2019-08-05

Total Pages: 725

ISBN-13: 0135121728

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"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

A Human's Guide to Machine Intelligence
A Human's Guide to Machine Intelligence

Author: Kartik Hosanagar

Publisher: Viking Adult

Published: 2019

Total Pages: 274

ISBN-13: 0525560882

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In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives.

Computational Linguistics and Intelligent Text Processing
Computational Linguistics and Intelligent Text Processing

Author: Alexander Gelbukh

Publisher: Springer Science & Business Media

Published: 2005-01-31

Total Pages: 845

ISBN-13: 3540245235

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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.