The Best-Selling OpenAI Books of All Time

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator. Key FeaturesExplore the OpenAI Gym toolkit and interface to use over 700 learning tasksImplement agents to solve simple to complex AI problemsStudy learning environments and discover how to create your ownBook DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken.

The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level. What you will learnExplore intelligent agents and learning environmentsUnderstand the basics of RL and deep RLGet started with OpenAI Gym and PyTorch for deep reinforcement learningDiscover deep Q learning agents to solve discrete optimal control tasksCreate custom learning environments for real-world problemsApply a deep actor-critic agent to drive a car autonomously in CARLAUse the latest learning environments and algorithms to upgrade your intelligent agent development skillsWho this book is forIf you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it. Table of Contents Introduction to Intelligent Agents and Learning EnvironmentsReinforcement Learning and Deep Reinforcement LearningGetting Started with OpenAI Gym and Deep Reinforcement LearningExploring the Gym and its FeaturesImplementing your First Learning Agent – Solving the Mountain Car problemImplementing an Intelligent Agent for Optimal Control using Deep Q-LearningCreating Custom OpenAI Gym Environments – Carla Driving SimulatorImplementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic AlgorithmExploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLabExploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based).

Get started with GPT-3 and the OpenAI API for natural language processing using JavaScript and Python. Key FeaturesUnderstand the power of potential GPT-3 language models and the risks involvedExplore core GPT-3 use cases such as text generation, classification, and semantic search using engaging examplesPlan and prepare a GPT-3 application for the OpenAI review process required for publishing a live applicationBook DescriptionGenerative 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 learnUnderstand what GPT-3 is and how it can be used for various NLP tasksGet a high-level introduction to GPT-3 and the OpenAI APIImplement JavaScript and Python code examples that call the OpenAI APIStructure GPT-3 prompts and options to get the best possible resultsSelect the right GPT-3 engine or model to optimize for speed and cost-efficiencyFind out which use cases would not be suitable for GPT-3Create a GPT-3-powered knowledge base application that follows OpenAI guidelinesWho this book is forExploring 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. Table of ContentsIntroducing GPT-3 and the OpenAI APIGPT-3 Applications and Use CasesWorking with the OpenAI PlaygroundWorking with the OpenAI APICalling the OpenAI API in CodeContent FilteringGenerating and Transforming TextClassifying and Categorizing TextBuilding a GPT-3 Powered Question-Answering AppGoing Live with OpenAI-Powered Apps.

In this short course, we take you on a fun, hands-on and pragmatic journey to learn how to build LLM powered apps using LangChain. You'll start building your first Generative AI app within minutes. Every section is recorded in a bite-sized manner and straight to the point as I don’t want to waste your time (and most certainly mine) on the content you don't need. In this course, we will cover:. What is LangChain. How does LangChain Work. Installation, Setup and Our First LangChain App. Building a Medium Article Generator App. Connecting to OpenAI LLM. Prompt Templates. Simple Chains. Sequential Chains. Agents. Chat with a Document.

Adding Memory (Chat History). Outputting the Chat History. Uploading Custom Documents. Loading Different Document Types (eg PDF, txt, docs). Chat with Youtube and more... The goal of this course is to teach you LangChain development in a manageable way without overwhelming you. We focus only on the essentials and cover the material in a hands-on practice manner for you to code along. Working Through This Course. This course is purposely broken down into short sections where the development process of each section will center on different essential topics. The course a practical hands on approach to learning through practice. You learn best when you code along with the examples.