The Datasets That Shaped Lamda
Download The Datasets That Shaped Lamda full books in PDF, epub, and Kindle. Read online free The Datasets That Shaped Lamda ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : StoryBuddiesPlay |
| Publisher | : StoryBuddiesPlay |
| Total Pages | : 65 |
| Release | : 2024-06-10 |
| Genre | : Computers |
| ISBN | : |
Unleash the power of language with LaMDA, Google's groundbreaking conversational AI! This comprehensive guide delves into the fascinating world of LaMDA's training data, the very foundation that fuels its ability to engage in natural and nuanced conversations. Dive deep into the vast ocean of text and code that shapes LaMDA's understanding, exploring the role of books, articles, web text, and even code in equipping LaMDA with the tools to navigate human communication. Uncover the secrets behind LaMDA's mastery of back-and-forth conversation. Discover how dialogue datasets brimming with real-world conversations, interviews, and chats teach LaMDA the art of turn-taking, understanding context, and responding with emotional intelligence. But the journey doesn't stop there. Explore the potential for bias within training data and the ongoing efforts to ensure fairness and inclusivity in LaMDA's responses. Peer into the inner workings of LaMDA with a focus on the art of context. See how LaMDA analyzes preceding words and phrases to build a mental model of the conversation, grasps the subtle cues of emotional tone and sarcasm, and dis disambiguates ambiguous situations to arrive at the most likely interpretation. Witness LaMDA's surprising ability to generate creative text formats, from poems and code to scripts, fueled by its analysis of vast amounts of creative writing styles. This exploration wouldn't be complete without addressing the crucial challenge of bias. Unearth the potential for bias present in training data and the ethical considerations surrounding it. Learn about the multifaceted approach employed to address bias, including curating diverse datasets, utilizing debiasing techniques, and prioritizing transparency and explainability in LaMDA's development. Finally, gaze into the future with LaMDA. Witness how continuous learning with new data allows LaMDA to refine its capabilities and stay up-to-date. Explore the importance of transparency and explainability (XAI) techniques in building trust and fostering a collaborative human-AI partnership. Uncover the vast potential applications of LaMDA in education, customer service, scientific research, and creative exploration. This comprehensive guide empowers you to understand LaMDA, the conversational AI poised to transform the way we interact with technology and access information. Embark on this journey of discovery and unlock the potential of language learning alongside LaMDA!
| Author | : Guillaume Coqueret |
| Publisher | : CRC Press |
| Total Pages | : 358 |
| Release | : 2023-08-08 |
| Genre | : Mathematics |
| ISBN | : 1000912809 |
a detailed presentation of the key machine learning tools use in finance a large scale coding tutorial with easily reproducible examples realistic applications on a large publicly available dataset all the key ingredients to perform a full portfolio backtest
| Author | : Uday Kamath |
| Publisher | : Springer Nature |
| Total Pages | : 496 |
| Release | : 2024 |
| Genre | : Artificial intelligence |
| ISBN | : 3031656474 |
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.
| Author | : Shai Avidan |
| Publisher | : Springer Nature |
| Total Pages | : 804 |
| Release | : 2022-11-01 |
| Genre | : Computers |
| ISBN | : 3031198271 |
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
| Author | : Xizhao Wang |
| Publisher | : Springer |
| Total Pages | : 460 |
| Release | : 2014-12-04 |
| Genre | : Computers |
| ISBN | : 3662456524 |
This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.
| Author | : James Warren |
| Publisher | : Simon and Schuster |
| Total Pages | : 481 |
| Release | : 2015-04-29 |
| Genre | : Computers |
| ISBN | : 1638351104 |
Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth
| Author | : Hamid R. Arabnia |
| Publisher | : Springer Nature |
| Total Pages | : 1152 |
| Release | : 2021-10-14 |
| Genre | : Technology & Engineering |
| ISBN | : 3030702960 |
The book presents the proceedings of two conferences: The 22nd International Conference on Artificial Intelligence (ICAI’20) and The 4th International Conference on Applied Cognitive Computing (ACC’20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020, and are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Topics include: deep learning; neural networks; brain models; cognitive science; natural language processing; fuzzy logic and soft computing (ICAI) and novel computationally intelligent algorithms; bio inspired cognitive algorithms; modeling human brain processing systems (ACC); and more. Authors include academics, researchers, and professionals. Presents the proceedings of two conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20); Includes the tracks: artificial intelligence and applied cognitive computing; Features papers from the 22nd International Conference on AI (ICAI’20) and the 4th International Conference on Applied Cognitive Computing (ACC’20).
| Author | : Amita Kapoor |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 699 |
| Release | : 2022-10-06 |
| Genre | : Computers |
| ISBN | : 1803245719 |
Build cutting edge machine and deep learning systems for the lab, production, and mobile devices Key FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook Description Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML. What you will learnLearn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras APIWho this book is for This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems. Some machine learning knowledge would be useful. We don't assume TF knowledge.
| Author | : Zoiner Tejada |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 411 |
| Release | : 2017-04-06 |
| Genre | : Computers |
| ISBN | : 1491956623 |
Helps users understand the breadth of Azure services by organizing them into a reference framework they can use when crafting their own big-data analytics solution.
| Author | : Haoxing Ren |
| Publisher | : Springer Nature |
| Total Pages | : 585 |
| Release | : 2023-01-01 |
| Genre | : Technology & Engineering |
| ISBN | : 303113074X |
This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.
