Openai Gpt For Python Developers 2nd Edition

The knowledge you'll acquire from this guide will be applicable to the current families of GPT models (GPT-3, GPT-3.5, GPT-4, etc.) and will likely also be relevant to GPT-5, should it ever be released. OpenAI provides APIs (Application Programming Interfaces) to access their AI. The goal of an API is to abstract the underlying models by creating a universal interface for all versions, allowing users to use GPT regardless of its version. This guide aims to provide a comprehensive, step-by-step tutorial on how to utilize GPT-3.5 and GPT-4 in your projects via this API. It also covers other models, such as Whisper and Text-to-Speech. If you're developing a chatbot, an AI assistant, or a web application that utilizes AI-generated data, this guide will assist you in achieving your objectives. If you have a basic understanding of the Python programming language and are willing to learn a few additional techniques, such as using Pandas Dataframes and some NLP methods, you possess all the necessary tools to start building intelligent systems with OpenAI tools. Rest assured, you don't need to be a data scientist, machine learning engineer, or AI expert to comprehend and implement the concepts, techniques, and tutorials presented in this guide. The explanations provided are straightforward and easy to understand, featuring simple Python code, examples, and hands-on exercises. This guide emphasizes practical, hands-on learning and is designed to assist readers in building real-world applications. It is example-driven and provides numerous practical examples to help readers understand the concepts and apply them to real-life scenarios to solve real-world problems. By the end of your learning journey, you will have developed applications such as: Fine-tuned, domain-specific chatbots. An intelligent conversational system with memory and context. A semantic modern search engine using RAG and other techniques. An intelligent coffee recommendation system based on your taste. A chatbot assistant to assist with Linux commands A fine-tuned news category prediction system. An AI-to-AI autonomous discussion system to simulate human-like conversations or solve problems An AI-based mental health coach trained on a large dataset of mental health conversations and more! By reading this guide and following the examples, you will be able to: Understand the different models available, and how and when to use each one. Generate human-like text for various purposes, such as answering questions, creating content, and other creative uses. Control the creativity of GPT models and adopt the best practices to generate high-quality text. Transform and edit the text to perform translation, formatting, and other useful tasks. Optimize the performance of GPT models using various parameters and options such as max_tokens, temperature, top_p, n, stream, logprobs, stop, presence_penalty, frequency_penalty, best_of, and others. Stem, lemmatize and reduce your costs when using the API. Understand Context Stuffing, chaining, and practice prompt engineering. Implement a chatbot with memory and context. Create prediction algorithms and zero-shot techniques and evaluate their accuracy. Understand, practice, and improve few-shot learning. Understand fine-tuning and leverage its power to create your own fine-tuned models. Understand and use fine-tuning best practices Practice training and classification techniques using GPT. Understand embedding and how companies such as Tesla and Notion are using it. Understand and implement semantic search, RAG, and other advanced tools and concepts. Integrate a Vector Database (e.g.: Weaviate) with your intelligent systems.

"OpenAI GPT for Python Developers" is your comprehensive guide to mastering the integration of OpenAI's GPT models into your Python projects, enhancing applications with various AI capabilities from chat completions to AI avatars. Key Features Strategies for optimizing and personalizing GPT models for specific applications. Insights into integrating additional OpenAI technologies like Whisper and Weaviate. Strong emphasis on responsible AI development and deployment. Book Description“OpenAI GPT for Python Developers” is meticulously crafted to provide Python developers with a deep dive into the mechanics and applications of GPT technology, beginning with a captivating narrative on the evolution of OpenAI and the fundamental workings of GPT models. As readers progress, they will be expertly guided through the essential steps of setting up a development environment tailored for AI innovations, coupled with insightful advice on selecting the most appropriate GPT model to suit specific project needs. The guide progresses into practical tutorials that cover the implementation of chat completions and the art of prompt engineering, providing a solid foundation in harnessing the capabilities of GPT for generating human-like text responses. Practical applications are further expanded with discussions on the creation of autonomous AI-to-AI dialogues, the development of AI avatars, and the strategic use of AI in interactive applications. In addition to technical skills, this book addresses the ethical implications and prospects of AI technologies, encouraging a holistic view of AI development. The guide is enriched with detailed examples, step-by-step tutorials, and comprehensive explanations that illuminate the theoretical aspects and emphasize practical implementation.What you will learn Set up the development environment for OpenAI GPT. Understand and choose the right GPT model for your needs. Implement advanced prompt engineering techniques. Explore embedding and advanced embedding examples. Utilize OpenAI's Whisper for speech recognition and translation. Integrate OpenAI TTS models for text-to-speech applications. Who this book is for This book is designed for readers at an intermediate to advanced level who have a basic understanding of machine learning concepts and are eager to expand their expertise in AI with a focus on OpenAI's technologies. Ideal for those involved in AI-driven projects, the book assumes familiarity with Python programming and a fundamental grasp of AI principles. It’s especially beneficial for developers aiming to integrate GPT models into applications, AI researchers, and technical professionals involved in AI product development.

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook Description With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects. What you will learnUnderstand core RL concepts including the methodologies, math, and codeTrain an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI GymTrain an agent to play Ms Pac-Man using a Deep Q NetworkLearn policy-based, value-based, and actor-critic methodsMaster the math behind DDPG, TD3, TRPO, PPO, and many othersExplore new avenues such as the distributional RL, meta RL, and inverse RLUse Stable Baselines to train an agent to walk and play Atari gamesWho this book is for If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.

This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading. FEATURES Offers an all-encompassing view of ChatGPT and GPT-4, from basics to advanced topics, including functionalities, capabilities, and limitations Contains Python 3.x code samples demonstrating the application of GPT-4 in real-world scenarios Provides a forward-looking perspective on Generative AI and its integration with data visualization and DALL-E Includes companion files with source code, data sets, and figures

Beginning Python Games Development, Second Edition teaches you how to create compelling games using Python and the PyGame games development library. It will teach you how to create visuals, do event handling, create 3D games, add media elements, and integrate OpenGL into your Python game. In this update to the first ever book to cover the popular open source PyGame games development library, you'll stand to gain valuable technical insights and follow along with the creation of a real-world, freely downloadable video game. Written by industry veterans and Python experts Will McGugan and Harrison Kinsley, this is a comprehensive, practical introduction to games development in Python. You can also capitalize upon numerous tips and tricks the authors have accumulated over their careers creating games for some of the world's largest game developers.

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

Get started with GPT-3 and the OpenAI API for natural language processing using JavaScript and Python Key Features: Understand the power of potential GPT-3 language models and the risks involved Explore core GPT-3 use cases such as text generation, classification, and semantic search using engaging examples Plan and prepare a GPT-3 application for the OpenAI review process required for publishing a live application Book Description: Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text that is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API. If you want to get hands-on with leveraging artificial intelligence for natural language processing (NLP) tasks, this easy-to-follow book will help you get started. Beginning with a high-level introduction to NLP and GPT-3, the book takes you through practical examples that show how to leverage the OpenAI API and GPT-3 for text generation, classification, and semantic search. You'll explore the capabilities of the OpenAI API and GPT-3 and find out which NLP use cases GPT-3 is best suited for. You'll also learn how to use the API and optimize requests for the best possible results. With examples focusing on the OpenAI Playground and easy-to-follow JavaScript and Python code samples, the book illustrates the possible applications of GPT-3 in production. By the end of this book, you'll understand the best use cases for GPT-3 and how to integrate the OpenAI API in your applications for a wide array of NLP tasks. What You Will Learn: Understand what GPT-3 is and how it can be used for various NLP tasks Get a high-level introduction to GPT-3 and the OpenAI API Implement JavaScript and Python code examples that call the OpenAI API Structure GPT-3 prompts and options to get the best possible results Select the right GPT-3 engine or model to optimize for speed and cost-efficiency Find out which use cases would not be suitable for GPT-3 Create a GPT-3-powered knowledge base application that follows OpenAI guidelines Who this book is for: Exploring GPT-3 is for anyone interested in natural language processing or learning GPT-3 with or without a technical background. Developers, product managers, entrepreneurs, and hobbyists looking to get to grips with NLP, AI, and GPT-3 will find this book useful. Basic computer skills are all you need to get the most out of this book.

No one knows the real world of modern day cops like Joseph D. McNamara. In his new novel, this former beat cop and big-city police chief takes you inside the maelstrom that America's front line foot soldiers face everyday--from the street snitches to the thugs in City Hall, from the deals that get cut to the danger that never lets you go. . . . Kevin McKay is a hometown boy who grew up to be a cop. Now he's out of the fire and into the heat--transferred from narcotics to a serial rape case that is turning into murder. But while McKay scours San Francisco from the Tenderloin to Chinatown for a perp known only as Ski Mask, a web of betrayal is being spun by the most dangerous enemies a cop can ever have--the ones who carry a badge. Trusting no one--not his bosses, not the rich lady he's falling in love with--McKay is fighting back against a death trap with his wits, his courage, and his honor . . . on streets stained forever with blood.