Generative Ai Foundations In Python

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Generative AI Foundations in Python

Author : Carlos Rodriguez
Publisher : Packt Publishing Ltd
ISBN 13 : 1835464912
Total Pages : 190 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Generative AI Foundations in Python by : Carlos Rodriguez

Download or read book Generative AI Foundations in Python written by Carlos Rodriguez and published by Packt Publishing Ltd. This book was released on 2024-07-26 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.

Artificial Intelligence with Python

Author : Prateek Joshi
Publisher : Packt Publishing Ltd
ISBN 13 : 1786469677
Total Pages : 437 pages
Book Rating : 4.7/5 (864 download)

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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.

Artificial Intelligence, IOT and Machine Learning

Author : Praveen Donepudi
Publisher :
ISBN 13 :
Total Pages : 72 pages
Book Rating : 4.7/5 (54 download)

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Book Synopsis Artificial Intelligence, IOT and Machine Learning by : Praveen Donepudi

Download or read book Artificial Intelligence, IOT and Machine Learning written by Praveen Donepudi and published by . This book was released on 2021-02-08 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Generative Deep Learning with Python

Author : Cuantum Technologies LLC
Publisher : Packt Publishing Ltd
ISBN 13 : 1836207123
Total Pages : 276 pages
Book Rating : 4.8/5 (362 download)

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Book Synopsis Generative Deep Learning with Python by : Cuantum Technologies LLC

Download or read book Generative Deep Learning with Python written by Cuantum Technologies LLC and published by Packt Publishing Ltd. This book was released on 2024-06-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications. Key Features Comprehensive coverage of deep learning and generative models. In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI. Practical coding exercises & interactive assignments to build your own generative models. Book DescriptionGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.What you will learn Develop a detailed understanding of deep learning fundamentals Implement and train Generative Adversarial Networks (GANs) Create & utilize Variational Autoencoders for data generation Apply autoregressive models for text generation Explore advanced topics & stay ahead in the field of generative AI Analyze and optimize the performance of generative models Who this book is for This course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial.

Generative AI with Python and TensorFlow

Author : Anand Vemula
Publisher : Independently Published
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.3/5 (321 download)

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Book Synopsis Generative AI with Python and TensorFlow by : Anand Vemula

Download or read book Generative AI with Python and TensorFlow written by Anand Vemula and published by Independently Published. This book was released on 2024-07-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models is a comprehensive resource for anyone looking to delve into the world of generative artificial intelligence. Introduction Overview of Generative AI: Understand the basic concepts, history, and significance of generative AI. Importance of Generative AI: Learn about the transformative potential of generative AI in various industries. Applications and Use Cases: Explore real-world applications of generative AI in fields such as art, music, text generation, and data augmentation. Overview of Python and TensorFlow: Get an introduction to the essential tools and libraries used for building generative AI models. Getting Started: Set up your development environment, install necessary libraries, and take your first steps with TensorFlow. Fundamentals of Machine Learning Supervised vs. Unsupervised Learning: Understand the differences and use cases of these two primary types of machine learning. Neural Networks Basics: Learn the fundamental concepts of neural networks and their role in AI. Introduction to Deep Learning: Dive deeper into the advanced techniques of deep learning and its applications in generative AI. Key Concepts in Generative AI: Familiarize yourself with the essential concepts and terminologies in generative AI. Generative Models Understanding Generative Models: Explore the theoretical foundations of generative models. Types of Generative Models: Learn about various types of generative models, including VAEs, GANs, autoregressive models, and flow-based models. Variational Autoencoders (VAEs): Delve into the theory behind VAEs, build and train VAEs with TensorFlow, and explore their use cases. Generative Adversarial Networks (GANs): Get introduced to GANs, understand their architecture, implement GANs with TensorFlow, and learn advanced GAN techniques. Autoregressive Models: Understand autoregressive models, implement them with TensorFlow, and explore their applications. Flow-based Models: Learn about flow-based models, build them with TensorFlow, and explore their practical applications. Advanced Topics Transfer Learning for Generative Models: Explore how transfer learning can be applied to generative models. Conditional Generative Models: Understand and implement models that generate outputs conditioned on specific inputs. Multimodal Generative Models: Learn about models that can generate multiple types of data simultaneously. Reinforcement Learning in Generative AI: Explore the intersection of reinforcement learning and generative AI. Practical Applications Image Generation and Style Transfer: Create stunning images and apply style transfer techniques. Text Generation and Natural Language Processing: Generate coherent and contextually relevant text using advanced NLP techniques. Music and Sound Generation: Compose music and generate new sounds using generative AI. Data Augmentation for Machine Learning: Improve your machine learning models by augmenting your datasets with generative models. Hands-On Projects Project 1: Creating Art with GANs: Step-by-step guide to building a GAN to generate art. Project 2: Text Generation with LSTM: Implement an LSTM model for generating text. Project 3: Building a VAE for Image Reconstruction: Learn how to build and train a VAE for image reconstruction. Project 4: Music Generation with RNNs: Create a music generation model using RNNs.

Generative AI with Python and TensorFlow 2

Author : Joseph Babcock
Publisher : Packt Publishing Ltd
ISBN 13 : 1800208502
Total Pages : 489 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Generative AI with Python and TensorFlow 2 by : Joseph Babcock

Download or read book Generative AI with Python and TensorFlow 2 written by Joseph Babcock and published by Packt Publishing Ltd. This book was released on 2021-04-30 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

Practical Generative AI with Python

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 123 pages
Book Rating : 4./5 ( download)

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Book Synopsis Practical Generative AI with Python by : Anand Vemula

Download or read book Practical Generative AI with Python written by Anand Vemula and published by Anand Vemula. This book was released on with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals of generative AI, providing an in-depth understanding of key concepts, algorithms, and techniques that power AI-driven content creation. Starting with an introduction to the basics of generative AI, the book explains the theoretical foundations and evolution of generative models, highlighting the significance of this technology in various domains such as image synthesis, text generation, and more. Readers will explore the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and understand their role in the development of generative models. The guide dives into essential Python libraries like TensorFlow, PyTorch, NumPy, and Pandas, offering a hands-on approach to building generative models from scratch. Each chapter is packed with practical examples, case studies, and real-world scenarios that demonstrate the application of these models in various fields, including art, music, and conversational AI. Key topics include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, autoregressive models, and transformer-based models like GPT. The book also addresses the ethical considerations surrounding generative AI, providing insights into the challenges of bias, fairness, and misinformation. Readers will benefit from step-by-step tutorials that guide them through the process of implementing and optimizing generative models, complete with code examples and hands-on exercises. Additionally, the book offers advanced techniques for improving model performance and stability, ensuring that readers are well-prepared to tackle complex AI projects. Whether you're a beginner looking to understand the basics of generative AI or an experienced developer aiming to enhance your skills, "Mastering Generative AI with Python: A Hands-On Guide" serves as an essential resource for anyone interested in the rapidly evolving field of generative AI.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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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

Python AI Programming

Author : Patrick J
Publisher : GitforGits
ISBN 13 : 8119177630
Total Pages : 260 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Python AI Programming by : Patrick J

Download or read book Python AI Programming written by Patrick J and published by GitforGits. This book was released on 2024-01-03 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable. Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence. Key Learnings Explore Python basics and AI integration for real-world application and career advancement. Experience the power of Python in AI with practical machine learning techniques. Practice Python's deep learning tools for innovative AI solution development. Dive into NLP with Python to revolutionize data interpretation and communication strategies. Simple yet practical understanding of reinforcement learning for strategic AI decision making. Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI. Harness Python's capabilities for creating AI applications with a focus on fairness and bias. Table of Content Introduction to Artificial Intelligence Python for AI Data as Fuel for AI Machine Learning Foundation Essentials of Deep Learning NLP and Computer Vision Hands-on Reinforcement Learning Ethics to AI

Python Natural Language Processing Cookbook

Author : Zhenya Antić
Publisher : Packt Publishing Ltd
ISBN 13 : 1803241446
Total Pages : 312 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Python Natural Language Processing Cookbook by : Zhenya Antić

Download or read book Python Natural Language Processing Cookbook written by Zhenya Antić and published by Packt Publishing Ltd. This book was released on 2024-09-13 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated to include three new chapters on transformers, natural language understanding (NLU) with explainable AI, and dabbling with popular LLMs from Hugging Face and OpenAI Key Features Leverage ready-to-use recipes with the latest LLMs, including Mistral, Llama, and OpenAI models Use LLM-powered agents for custom tasks and real-world interactions Gain practical, in-depth knowledge of transformers and their role in implementing various NLP tasks with open-source and advanced LLMs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionHarness the power of Natural Language Processing to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust and transparency in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.What you will learn Understand fundamental NLP concepts along with their applications using examples in Python Classify text quickly and accurately with rule-based and supervised methods Train NER models and perform sentiment analysis to identify entities and emotions in text Explore topic modeling and text visualization to reveal themes and relationships within text Leverage Hugging Face and OpenAI LLMs to perform advanced NLP tasks Use question-answering techniques to handle both open and closed domains Apply XAI techniques to better understand your model predictions Who this book is for This updated edition of the Python Natural Language Processing Cookbook is for data scientists, machine learning engineers, and developers with a background in Python. Whether you’re looking to learn NLP techniques, extract valuable insights from textual data, or create foundational applications, this book will equip you with basic to intermediate skills. No prior NLP knowledge is necessary to get started. All you need is familiarity with basic programming principles. For seasoned developers, the updated sections offer the latest on transformers, explainable AI, and Generative AI with LLMs.

Handbook of Constraint Programming

Author : Francesca Rossi
Publisher : Elsevier
ISBN 13 : 0080463800
Total Pages : 977 pages
Book Rating : 4.0/5 (84 download)

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Book Synopsis Handbook of Constraint Programming by : Francesca Rossi

Download or read book Handbook of Constraint Programming written by Francesca Rossi and published by Elsevier. This book was released on 2006-08-18 with total page 977 pages. Available in PDF, EPUB and Kindle. Book excerpt: Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. - Covers the whole field of constraint programming- Survey-style chapters- Five chapters on applications

Generative AI with Python and TensorFlow 2

Author : Joseph Babcock
Publisher :
ISBN 13 : 9781800200883
Total Pages : 488 pages
Book Rating : 4.2/5 (8 download)

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Book Synopsis Generative AI with Python and TensorFlow 2 by : Joseph Babcock

Download or read book Generative AI with Python and TensorFlow 2 written by Joseph Babcock and published by . This book was released on 2021-04-30 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Packed with intriguing real-world projects as well as theory, Generative AI with Python and TensorFlow 2 enables you to leverage artificial intelligence creatively and generate human-like data in the form of speech, text, images, and music.

Python and R for the Modern Data Scientist

Author : Rick J. Scavetta
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492093378
Total Pages : 199 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Python and R for the Modern Data Scientist by : Rick J. Scavetta

Download or read book Python and R for the Modern Data Scientist written by Rick J. Scavetta and published by "O'Reilly Media, Inc.". This book was released on 2021-06-22 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together

Generative Deep Learning with Python

Author : Cuantum Technologies LLC
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.3/5 (955 download)

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Book Synopsis Generative Deep Learning with Python by : Cuantum Technologies LLC

Download or read book Generative Deep Learning with Python written by Cuantum Technologies LLC and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to a journey where artificial intelligence meets creativity, where deep learning algorithms dream, and where you are the architect of these dreams. Introducing Generative Deep Learning with Python: Unleashing the Creative Power of AI - your comprehensive guide to the enchanting world of generative models. Have you ever been mesmerized by AI-created artwork, deep fake videos, or the uncanny ability of platforms like Spotify to match your music taste? At the heart of these technologies lie Generative Models, a cutting-edge AI application that's revolutionizing industries. This book is a comprehensive guide that explores this revolutionary domain. It promises to take you on a journey that cuts through the complexity and illuminates the principles that power generative models. It's a ticket to a world where art meets science, creativity aligns with technology, and AI dreams become a reality. This book is more than a guide; it's a thrilling adventure into this realm. Our journey starts with the fundamentals, demystifying concepts like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive models. This is a ticket for everyone, whether you're a seasoned AI practitioner or an enthusiastic beginner. Interest deepened? Get your hands on the three exciting projects that form the bedrock of our book: Face Generation with GANs, Handwritten Digit Generation with VAEs, and Text Generation with Autoregressive Models. These practical projects give you the opportunity to apply your knowledge and gain insights into the process of building and training generative models. The desire for more? Delve into advanced topics, exploring challenges, solutions, and prospects. From understanding and tackling the notorious problem of Mode Collapse to incorporating domain knowledge into your generative models, the book covers it all.

UX for Enterprise ChatGPT Solutions

Author : Richard H. Miller
Publisher : Packt Publishing Ltd
ISBN 13 : 1835463800
Total Pages : 446 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis UX for Enterprise ChatGPT Solutions by : Richard H. Miller

Download or read book UX for Enterprise ChatGPT Solutions written by Richard H. Miller and published by Packt Publishing Ltd. This book was released on 2024-09-06 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create engaging AI experiences by mastering ChatGPT for business and leveraging user interface design practices, research methods, prompt engineering, the feeding lifecycle, and more Key Features Learn in-demand design thinking and user research techniques applicable to all conversational AI platforms Measure the quality and evaluate ChatGPT from a customer’s perspective for optimal user experience Set up and use your secure private data, documents, and materials to enhance your ChatGPT models Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany enterprises grapple with new technology, often hopping on the bandwagon only to abandon it when challenges emerge. This book is your guide to seamlessly integrating ChatGPT into enterprise solutions with a UX-centered approach. UX for Enterprise ChatGPT Solutions empowers you to master effective use case design and adapt UX guidelines through an engaging learning experience. Discover how to prepare your content for success by tailoring interactions to match your audience’s voice, style, and tone using prompt-engineering and fine-tuning. For UX professionals, this book is the key to anchoring your expertise in this evolving field. Writers, researchers, product managers, and linguists will learn to make insightful design decisions. You’ll explore use cases like ChatGPT-powered chat and recommendation engines, while uncovering the AI magic behind the scenes. The book introduces a care and feeding model, enabling you to leverage feedback and monitoring to iterate and refine any Large Language Model solution. Packed with hundreds of tips and tricks, this guide will help you build a continuous improvement cycle suited for AI solutions. By the end, you’ll know how to craft powerful, accurate, responsive, and brand-consistent generative AI experiences, revolutionizing your organization’s use of ChatGPT.What you will learn Align with user needs by applying design thinking to tailor ChatGPT to meet customer expectations Harness user research to enhance chatbots and recommendation engines Track quality metrics and learn methods to evaluate and monitor ChatGPT's quality and usability Establish and maintain a uniform style and tone with prompt engineering and fine-tuning Apply proven heuristics by monitoring and assessing the UX for conversational experiences with trusted methods Refine continuously by implementing an ongoing process for chatbot care and feeding Who this book is for This book is for user experience designers, product managers, and product owners of business and enterprise ChatGPT solutions who are interested in learning how to design and implement ChatGPT-4 solutions for enterprise needs. You should have a basic-to-intermediate level of understanding in UI/UX design concepts and fundamental knowledge of ChatGPT-4 and its capabilities.

Deep Learning with Python

Author : Chao Pan
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781721250974
Total Pages : 124 pages
Book Rating : 4.2/5 (59 download)

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Book Synopsis Deep Learning with Python by : Chao Pan

Download or read book Deep Learning with Python written by Chao Pan and published by Createspace Independent Publishing Platform. This book was released on 2016-06-14 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (will soon return to 24.77 $) *****Are you thinking of learning deep Learning using Python? (For Beginners Only) If you are looking for a beginners guide to learn deep learning, in just a few hours, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach, which would lead to better mental representations.Step-by-Step Guide and Visual Illustrations and ExamplesThis book and the accompanying examples, you would be well suited to tackle problems, which pique your interests using machine learning and deep learning models. Book Objectives This book will help you: Have an appreciation for deep learning and an understanding of their fundamental principles. Have an elementary grasp of deep learning concepts and algorithms. Have achieved a technical background in deep learning and neural networks using Python. Target UsersThe book designed for a variety of target audiences. Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and deep learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction What is Artificial Intelligence, Machine Learning and Deep Learning? Mathematical Foundations of Deep Learning Understanding Machine Learning Models Evaluation of Machine Learning Models: Overfitting, Underfitting, Bias Variance Tradeoff Fully Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning Introduction to Deep Neural Networks with Keras A First Look at Neural Networks in Keras Introduction to Pytorch The Pytorch Deep Learning Framework Your First Neural Network in Pytorch Deep Learning for Computer Vision Build a Convolutional Neural Network Deep Learning for Natural Language Processing Working with Sequential Data Build a Recurrent Neural Network Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Deep Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is an excellent book, it is a very good introduction to deep learning and neural networks. The concepts and terminology are clearly explained. The book also points out several good locations on the internet where users can obtain more information. I was extremely happy with this book and I recommend it for all beginners" - Prof. Alain Simon, EDHEC Business School. Statistician and DataScientist.

AI Foundations of GPT

Author : Jon Adams
Publisher : Green Mountain Computing
ISBN 13 :
Total Pages : 143 pages
Book Rating : 4./5 ( download)

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Book Synopsis AI Foundations of GPT by : Jon Adams

Download or read book AI Foundations of GPT written by Jon Adams and published by Green Mountain Computing. This book was released on with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the heart of artificial intelligence with "AI Foundations of GPT," a groundbreaking book that charts the journey of Generative Pre-trained Transformers (GPT) from their conceptual inception to their role as cornerstones of modern AI applications. This meticulously crafted text serves as both a historical narrative and a forward-looking discussion, exploring the myriad ways in which GPT technology is reshaping our digital landscape. Key Features: Comprehensive Coverage: From the AI revolution to the future of GPT, each chapter is dedicated to a different facet of GPT technology, ensuring readers gain a well-rounded understanding of its complexities and capabilities. Accessible Explanations: Designed to cater to both AI aficionados and newcomers, the book explains the technical underpinnings of GPT models in an engaging and understandable manner. Future-Oriented: Offers a peek into the potential advancements and challenges that lie ahead for GPT technology, encouraging readers to ponder its implications for society and industry. Chapters: The AI Revolution: An overview of how artificial intelligence has evolved, setting the stage for the emergence of GPT. Understanding GPT: Breaks down the basics of Generative Pre-trained Transformers, explaining what they are and why they matter. The Mechanics of GPT: Delves into the technical aspects of how GPT models work, from algorithms to neural networks. Training GPT Models: Discusses the process of training GPT models, highlighting the resources and methodologies involved. Applications of GPT: Explores the diverse applications of GPT in various fields such as literature, customer service, and software development. Ethical Considerations: Examines the ethical dilemmas and considerations surrounding the use of GPT technology. The Business of GPT: Analyzes the economic landscape of GPT, including its impact on industries and business models. Limitations and Challenges: Acknowledges the limitations of current GPT models and the challenges facing their development. The Future of GPT: Speculates on the future advancements of GPT technology and its potential societal impacts. Whether you're deeply embedded in the world of AI or simply curious about the technologies shaping our future, "AI Foundations of GPT" offers a rich, insightful exploration of one of the most significant developments in artificial intelligence. Embark on this journey to understand not just the mechanics of GPT, but its profound implications on our world.