Pretrain Vision and Large Language Models in Python

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Publisher : Packt Publishing Ltd
ISBN 13 : 1804612545
Total Pages : 258 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Pretrain Vision and Large Language Models in Python by : Emily Webber

Download or read book Pretrain Vision and Large Language Models in Python written by Emily Webber and published by Packt Publishing Ltd. This book was released on 2023-05-31 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examples Key Features Learn to develop, train, tune, and apply foundation models with optimized end-to-end pipelines Explore large-scale distributed training for models and datasets with AWS and SageMaker examples Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring Book Description Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization. With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you'll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models. You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines. By the end of this book, you'll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future. What you will learn Find the right use cases and datasets for pretraining and fine-tuning Prepare for large-scale training with custom accelerators and GPUs Configure environments on AWS and SageMaker to maximize performance Select hyperparameters based on your model and constraints Distribute your model and dataset using many types of parallelism Avoid pitfalls with job restarts, intermittent health checks, and more Evaluate your model with quantitative and qualitative insights Deploy your models with runtime improvements and monitoring pipelines Who this book is for If you're a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way.

Time Series Indexing

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Publisher : Packt Publishing Ltd
ISBN 13 : 1838822879
Total Pages : 249 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Time Series Indexing by : Mihalis Tsoukalos

Download or read book Time Series Indexing written by Mihalis Tsoukalos and published by Packt Publishing Ltd. This book was released on 2023-06-30 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and use the most popular time series index available today with Python to search and join time series at the subsequence level Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to implement algorithms and techniques from research papers Get to grips with building time series indexes using iSAX Leverage iSAX to solve real-world time series problems Book Description Time series are everywhere, ranging from financial data and system metrics to weather stations and medical records. Being able to access, search, and compare time series data quickly is essential, and this comprehensive guide enables you to do just that by helping you explore SAX representation and the most effective time series index, iSAX. The book begins by teaching you about the implementation of SAX representation in Python as well as the iSAX index, along with the required theory sourced from academic research papers. The chapters are filled with figures and plots to help you follow the presented topics and understand key concepts easily. But what makes this book really great is that it contains the right amount of knowledge about time series indexing using the right amount of theory and practice so that you can work with time series and develop time series indexes successfully. Additionally, the presented code can be easily ported to any other modern programming language, such as Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript. By the end of this book, you'll have learned how to harness the power of iSAX and SAX representation to efficiently index and analyze time series data and will be equipped to develop your own time series indexes and effectively work with time series data. What you will learn Find out how to develop your own Python packages and write simple Python tests Understand what a time series index is and why it is useful Gain a theoretical and practical understanding of operating and creating time series indexes Discover how to use SAX representation and the iSAX index Find out how to search and compare time series Utilize iSAX visualizations to aid in the interpretation of complex or large time series Who this book is for This book is for practitioners, university students working with time series, researchers, and anyone looking to learn more about time series. Basic knowledge of UNIX, Linux, and Python and an understanding of basic programming concepts are needed to grasp the topics in this book. This book will also be handy for people who want to learn how to read research papers, learn from them, and implement their algorithms.

The Regularization Cookbook

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Publisher : Packt Publishing Ltd
ISBN 13 : 1837639728
Total Pages : 424 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis The Regularization Cookbook by : Vincent Vandenbussche

Download or read book The Regularization Cookbook written by Vincent Vandenbussche and published by Packt Publishing Ltd. This book was released on 2023-07-31 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3 Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to diagnose the need for regularization in any machine learning model Regularize different ML models using a variety of techniques and methods Enhance the functionality of your models using state of the art computer vision and NLP techniques Book Description Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you'll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You'll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you'll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you'll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you'll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E. By the end of this book, you'll be armed with different regularization techniques to apply to your ML and DL models. What you will learn Diagnose overfitting and the need for regularization Regularize common linear models such as logistic regression Understand regularizing tree-based models such as XGBoos Uncover the secrets of structured data to regularize ML models Explore general techniques to regularize deep learning models Discover specific regularization techniques for NLP problems using transformers Understand the regularization in computer vision models and CNN architectures Apply cutting-edge computer vision regularization with generative models Who this book is for This book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite.

Transformers for Natural Language Processing and Computer Vision

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Publisher : Packt Publishing Ltd
ISBN 13 : 1805123742
Total Pages : 731 pages
Book Rating : 4.8/5 (51 download)

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Book Synopsis Transformers for Natural Language Processing and Computer Vision by : Denis Rothman

Download or read book Transformers for Natural Language Processing and Computer Vision written by Denis Rothman and published by Packt Publishing Ltd. This book was released on 2024-02-29 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AI Key Features Compare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your project Apply RAG with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases Purchase of the print or Kindle book includes a free eBook in PDF format Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.What you will learn Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E Fine-tune BERT, GPT, and PaLM 2 models Learn about different tokenizers and the best practices for preprocessing language data Pretrain a RoBERTa model from scratch Implement retrieval augmented generation and rules bases to mitigate hallucinations Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V Who this book is for This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field. Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.

Deep Learning

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Author :
Publisher : Rob Botwright
ISBN 13 : 1839386258
Total Pages : 261 pages
Book Rating : 4.8/5 (393 download)

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Book Synopsis Deep Learning by : Rob Botwright

Download or read book Deep Learning written by Rob Botwright and published by Rob Botwright. This book was released on 101-01-01 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing the Ultimate AI Book Bundle: Deep Learning, Computer Vision, Python Machine Learning, and Neural Networks Are you ready to embark on an exhilarating journey into the world of artificial intelligence, deep learning, and computer vision? Look no further! Our carefully curated book bundle, "DEEP LEARNING: COMPUTER VISION, PYTHON MACHINE LEARNING AND NEURAL NETWORKS," offers you a comprehensive roadmap to AI mastery. BOOK 1 - DEEP LEARNING DEMYSTIFIED: A BEGINNER'S GUIDE 🚀 Perfect for beginners, this book dismantles the complexities of deep learning. From neural networks to Python programming, you'll build a strong foundation in AI. BOOK 2 - MASTERING COMPUTER VISION WITH DEEP LEARNING 🌟 Dive into the captivating world of computer vision. Unlock the secrets of image processing, convolutional neural networks (CNNs), and object recognition. Harness the power of visual intelligence! BOOK 3 - PYTHON MACHINE LEARNING AND NEURAL NETWORKS: FROM NOVICE TO PRO 📊 Elevate your skills with this intermediate volume. Delve into data preprocessing, supervised and unsupervised learning, and become proficient in training neural networks. BOOK 4 - ADVANCED DEEP LEARNING: CUTTING-EDGE TECHNIQUES AND APPLICATIONS 🔥 Ready to conquer advanced techniques? Learn optimization strategies, tackle common deep learning challenges, and explore real-world applications shaping the future. 🎉 What You'll Gain: · A strong foundation in deep learning · Proficiency in computer vision · Mastery of Python machine learning · Advanced deep learning skills · Real-world application knowledge · Cutting-edge AI insights 📚 Why Choose Our Book Bundle? · Expertly curated content · Beginner to expert progression · Clear explanations and hands-on examples · Comprehensive coverage of AI topics · Practical real-world applications · Stay ahead with emerging AI trends 🌐 Who Should Grab This Bundle? · Beginners eager to start their AI journey · Intermediate learners looking to expand their skill set · Experts seeking advanced deep learning insights · Anyone curious about AI's limitless possibilities 📦 Limited-Time Offer: Get all four books in one bundle and save! Don't miss this chance to accelerate your AI knowledge and skills. 🔒 Secure Your AI Mastery: Click "Add to Cart" now and embark on an educational adventure that will redefine your understanding of artificial intelligence. Your journey to AI excellence begins here!

Pattern Recognition and Machine Learning

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Author :
Publisher : Springer
ISBN 13 : 9781493938438
Total Pages : 0 pages
Book Rating : 4.9/5 (384 download)

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Book Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop

Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Deep Learning for Natural Language Processing

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Author :
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 413 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning for Natural Language Processing by : Jason Brownlee

Download or read book Deep Learning for Natural Language Processing written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2017-11-21 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.

Speech & Language Processing

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Publisher : Pearson Education India
ISBN 13 : 9788131716724
Total Pages : 912 pages
Book Rating : 4.7/5 (167 download)

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Book Synopsis Speech & Language Processing by : Dan Jurafsky

Download or read book Speech & Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Transformers for Natural Language Processing

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Publisher : Packt Publishing Ltd
ISBN 13 : 1800568630
Total Pages : 385 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Transformers for Natural Language Processing by : Denis Rothman

Download or read book Transformers for Natural Language Processing written by Denis Rothman and published by Packt Publishing Ltd. This book was released on 2021-01-29 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.

Getting Started with Google BERT

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Publisher : Packt Publishing Ltd
ISBN 13 : 1838826238
Total Pages : 340 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Getting Started with Google BERT by : Sudharsan Ravichandiran

Download or read book Getting Started with Google BERT written by Sudharsan Ravichandiran and published by Packt Publishing Ltd. This book was released on 2021-01-22 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library Key FeaturesExplore the encoder and decoder of the transformer modelBecome well-versed with BERT along with ALBERT, RoBERTa, and DistilBERTDiscover how to pre-train and fine-tune BERT models for several NLP tasksBook Description BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google's BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer’s encoder and decoder work. You’ll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you’ll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You'll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you'll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT. By the end of this BERT book, you’ll be well-versed with using BERT and its variants for performing practical NLP tasks. What you will learnUnderstand the transformer model from the ground upFind out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasksGet hands-on with BERT by learning to generate contextual word and sentence embeddingsFine-tune BERT for downstream tasksGet to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT modelsGet the hang of the BERT models based on knowledge distillationUnderstand cross-lingual models such as XLM and XLM-RExplore Sentence-BERT, VideoBERT, and BARTWho this book is for This book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book.

Dive Into Deep Learning

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Author :
Publisher : Corwin Press
ISBN 13 : 1544385404
Total Pages : 297 pages
Book Rating : 4.5/5 (443 download)

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Book Synopsis Dive Into Deep Learning by : Joanne Quinn

Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.

Practical Machine Learning with Rust

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Author :
Publisher : Apress
ISBN 13 : 1484251210
Total Pages : 362 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Practical Machine Learning with Rust by : Joydeep Bhattacharjee

Download or read book Practical Machine Learning with Rust written by Joydeep Bhattacharjee and published by Apress. This book was released on 2019-12-10 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

Deep Learning With Python

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Author :
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 266 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning With Python by : Jason Brownlee

Download or read book Deep Learning With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-05-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.

Statistical Significance Testing for Natural Language Processing

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Author :
Publisher : Springer Nature
ISBN 13 : 3031021746
Total Pages : 98 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Statistical Significance Testing for Natural Language Processing by : Rotem Dror

Download or read book Statistical Significance Testing for Natural Language Processing written by Rotem Dror and published by Springer Nature. This book was released on 2022-06-01 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.

Document Analysis and Recognition - ICDAR 2023

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Author :
Publisher : Springer Nature
ISBN 13 : 3031417348
Total Pages : 568 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Document Analysis and Recognition - ICDAR 2023 by : Gernot A. Fink

Download or read book Document Analysis and Recognition - ICDAR 2023 written by Gernot A. Fink and published by Springer Nature. This book was released on 2023-08-18 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: This six-volume set of LNCS 14187, 14188, 14189, 14190, 14191 and 14192 constitutes the refereed proceedings of the 17th International Conference on Document Analysis and Recognition, ICDAR 2021, held in San José, CA, USA, in August 2023. The 53 full papers were carefully reviewed and selected from 316 submissions, and are presented with 101 poster presentations. The papers are organized into the following topical sections: Graphics Recognition, Frontiers in Handwriting Recognition, Document Analysis and Recognition.

Machine Learning with PyTorch and Scikit-Learn

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Author :
Publisher : Packt Publishing Ltd
ISBN 13 : 1801816387
Total Pages : 775 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka

Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Deep Learning for Computer Vision

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Author :
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 564 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning for Computer Vision by : Jason Brownlee

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.