Generative Ai Foundations In Python
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| Author | : Carlos Rodriguez |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 190 |
| Release | : 2024-07-26 |
| Genre | : Computers |
| ISBN | : 1835464912 |
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.
| Author | : Prateek Joshi |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 437 |
| Release | : 2017-01-27 |
| Genre | : Computers |
| ISBN | : 1786469677 |
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.
| Author | : Jeremy Howard |
| Publisher | : O'Reilly Media |
| Total Pages | : 624 |
| Release | : 2020-06-29 |
| Genre | : Computers |
| ISBN | : 1492045497 |
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
| Author | : Martin Yanev |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 252 |
| Release | : 2024-10-04 |
| Genre | : Computers |
| ISBN | : 1835884016 |
Improve your app development skills by building a ChatGPT clone, code bug fixer, quiz generator, translation app, email auto-reply, PowerPoint generator, and more Key Features Transition into an expert AI developer by mastering ChatGPT concepts, including fine-tuning and integrations Gain hands-on experience through real-world projects covering a wide range of AI applications Implement payment systems in your applications by integrating the ChatGPT API with Stripe Purchase of the print or Kindle book includes a free PDF eBook Book Description Unlock the power of AI in your applications with ChatGPT with this practical guide that shows you how to seamlessly integrate OpenAI APIs into your projects, enabling you to navigate complex APIs and ensure seamless functionality with ease. This new edition is updated with key topics such as OpenAI Embeddings, which’ll help you understand the semantic relationships between words and phrases. You’ll find out how to use ChatGPT, Whisper, and DALL-E APIs through 10 AI projects using the latest OpenAI models, GPT-3.5, and GPT-4, with Visual Studio Code as the IDE. Within these projects, you’ll integrate ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. You’ll get to grips with NLP tasks, build a ChatGPT clone, and create an AI code bug-fixing SaaS app. The chapters will also take you through speech recognition, text-to-speech capabilities, language translation, generating email replies, creating PowerPoint presentations, and fine-tuning ChatGPT, along with adding payment methods by integrating the ChatGPT API with Stripe. By the end of this book, you’ll be able to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs. What you will learn Develop a solid foundation in using the OpenAI API for NLP tasks Build, deploy, and integrate payments into various desktop and SaaS AI applications Integrate ChatGPT with frameworks such as Flask, Django, and Microsoft Office APIs Unleash your creativity by integrating DALL-E APIs to generate stunning AI art within your desktop apps Experience the power of Whisper API's speech recognition and text-to-speech features Find out how to fine-tune ChatGPT models for your specific use case Master AI embeddings to measure the relatedness of text strings Who this book is for This book is for a diverse range of professionals, including programmers, entrepreneurs, and software enthusiasts. Beginner programmers, Python developers exploring AI applications with ChatGPT, software developers integrating AI technology, and web developers creating AI-powered web applications with ChatGPT will find this book beneficial. Scholars and researchers working on AI projects with ChatGPT will also find it valuable. Basic knowledge of Python and familiarity with APIs is needed to understand the topics covered in this book.
| Author | : Zhenya Antić |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 312 |
| Release | : 2024-09-13 |
| Genre | : Computers |
| ISBN | : 1803241446 |
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.
| Author | : Joseph Babcock |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 489 |
| Release | : 2021-04-30 |
| Genre | : Computers |
| ISBN | : 1800208502 |
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.
| Author | : Richard H. Miller |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 446 |
| Release | : 2024-09-06 |
| Genre | : Computers |
| ISBN | : 1835463800 |
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 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 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.
| Author | : Francois Chollet |
| Publisher | : Simon and Schuster |
| Total Pages | : 597 |
| Release | : 2017-11-30 |
| Genre | : Computers |
| ISBN | : 1638352046 |
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
| Author | : Hadelin de Ponteves |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 361 |
| Release | : 2019-11-29 |
| Genre | : Computers |
| ISBN | : 1838645551 |
Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key FeaturesLearn from friendly, plain English explanations and practical activitiesPut ideas into action with 5 hands-on projects that show step-by-step how to build intelligent softwareUse AI to win classic video games and construct a virtual self-driving carBook Description Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. What you will learnMaster the basics of AI without any previous experienceBuild fun projects, including a virtual-self-driving car and a robot warehouse workerUse AI to solve real-world business problemsLearn how to code in PythonDiscover the 5 principles of reinforcement learningCreate your own AI toolkitWho this book is for If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).
| Author | : Zsolt Nagy |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 330 |
| Release | : 2018-12-12 |
| Genre | : Computers |
| ISBN | : 1789809207 |
Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
