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Author: Gilad James, PhD Publisher: Gilad James Mystery School ISBN: 8942098088 Category : Science Languages : en Pages : 81
Book Description
OpenAI is a non-profit organization which aims to advance artificial intelligence (AI) in a way that benefits humanity as a whole. It was founded in 2015 by a group of prominent figures in the tech industry, including Elon Musk and Sam Altman. OpenAI has a collaborative approach, partnering with industry leaders to develop AI in a safe and ethical manner. The organization is focused on developing AI technologies that can be used to tackle global issues such as climate change, poverty, and disease. One of the main goals of OpenAI is to create cutting-edge AI that can be used to solve real-world problems. This is achieved through a combination of research and development, industry partnerships, and open-source tools and frameworks that can be used by developers and researchers around the world. OpenAI is also focused on ensuring that AI technology is developed in a responsible and ethical manner. This includes creating safety mechanisms to prevent AI systems from causing harm, as well as ensuring that AI is designed to benefit society as a whole and not just a small subset of individuals. Ultimately, OpenAI's vision is to create a future where AI is used to solve humanity's biggest challenges, improve the quality of life for all people, and create a more equitable and sustainable world.
Author: Gilad James, PhD Publisher: Gilad James Mystery School ISBN: 8942098088 Category : Science Languages : en Pages : 81
Book Description
OpenAI is a non-profit organization which aims to advance artificial intelligence (AI) in a way that benefits humanity as a whole. It was founded in 2015 by a group of prominent figures in the tech industry, including Elon Musk and Sam Altman. OpenAI has a collaborative approach, partnering with industry leaders to develop AI in a safe and ethical manner. The organization is focused on developing AI technologies that can be used to tackle global issues such as climate change, poverty, and disease. One of the main goals of OpenAI is to create cutting-edge AI that can be used to solve real-world problems. This is achieved through a combination of research and development, industry partnerships, and open-source tools and frameworks that can be used by developers and researchers around the world. OpenAI is also focused on ensuring that AI technology is developed in a responsible and ethical manner. This includes creating safety mechanisms to prevent AI systems from causing harm, as well as ensuring that AI is designed to benefit society as a whole and not just a small subset of individuals. Ultimately, OpenAI's vision is to create a future where AI is used to solve humanity's biggest challenges, improve the quality of life for all people, and create a more equitable and sustainable world.
Author: Steve Tingiris Publisher: Packt Publishing Ltd ISBN: 1800565496 Category : Computers Languages : en Pages : 296
Book Description
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 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 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 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.
Author: Sayon Dutta Publisher: Packt Publishing Ltd ISBN: 1788830717 Category : Computers Languages : en Pages : 327
Book Description
Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in robotics Who this book is for If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.
Author: Ian Goodfellow Publisher: MIT Press ISBN: 0262337371 Category : Computers Languages : en Pages : 801
Book Description
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Author: Jeremy Howard Publisher: O'Reilly Media ISBN: 1492045497 Category : Computers Languages : en Pages : 624
Book Description
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: Praveen Palanisamy Publisher: Packt Publishing Ltd ISBN: 1788835131 Category : Computers Languages : en Pages : 246
Book Description
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 Features Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks Implement agents to solve simple to complex AI problems Study learning environments and discover how to create your own Book Description Many 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 learn Explore intelligent agents and learning environments Understand the basics of RL and deep RL Get started with OpenAI Gym and PyTorch for deep reinforcement learning Discover deep Q learning agents to solve discrete optimal control tasks Create custom learning environments for real-world problems Apply a deep actor-critic agent to drive a car autonomously in CARLA Use the latest learning environments and algorithms to upgrade your intelligent agent development skills Who this book is for If 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.
Author: Richard S. Sutton Publisher: MIT Press ISBN: 0262352702 Category : Computers Languages : en Pages : 549
Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author: Khalid, Saifullah Publisher: IGI Global ISBN: 1799827208 Category : Technology & Engineering Languages : en Pages : 388
Book Description
Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies. Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students.
Author: Prateek Joshi Publisher: Packt Publishing Ltd ISBN: 1786469677 Category : Computers Languages : en Pages : 437
Book Description
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: David Foster Publisher: "O'Reilly Media, Inc." ISBN: 1492041890 Category : Computers Languages : en Pages : 301
Book Description
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Author: Gilad James, PhD
Author: Gilad James, PhD
Author: Steve Tingiris
Author: Sayon Dutta
Author: Ian Goodfellow
Author: Jeremy Howard
Author: Praveen Palanisamy
Author: Richard S. Sutton
Author: Khalid, Saifullah
Author: Prateek Joshi
Author: David Foster