Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape ... Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Found inside – Page 161... http://mccormickml.com/assets/word2vec/Alex_Minnaar_Word2Vec_Tutorial_ ... and Negative Sampling: https://arxiv.org/ abs/1410.8251 Sebastian Ruder, ... Found insideWhat you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement ... Found inside – Page 178The dataset consists of 12865 positive samples and 16198 negative samples. ... 4.2 Implementation The proposed SiamGCN is implemented by Pytorch [18]. Found inside – Page iThis book is a good starting point for people who want to get started in deep learning for NLP. Found inside – Page 240Word2Vec(sentences=sentences, sg=1, # 0 for CBOW and 1 for Skip-gram window=5, # the size of the context window negative=5, # negative sampling word count ... Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch. This book is ideal if you want to rapidly add PyTorch to your deep learning toolset. This book explores a once-popular picture story by Gordon Parks and the extraordinary chain of events it prompted. This book provides an introduction to artificial intelligence (AI) and deep learning (DL), IBM PowerAI, and components of IBM PowerAI, deploying IBM PowerAI, guidelines for working with data and creating models, an introduction to IBM ... Found inside – Page 1But 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? Found inside – Page iWhat You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who ... Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Found inside – Page 293Of course, you can also use TensorFlow and PyTorch to train different kinds of embeddings. ... word2vec-tutorial-part-2-negative-sampling. Found inside – Page iThe second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Found insideThis book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement ... This book will help you take your first steps when it comes to training efficient deep learning models, and apply them in various practical scenarios. You will model, train and deploy . The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field ... Found insideAbout This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on ... Found insideThis book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2015, which was held in Copenahgen, Denmark, in October 2015. The book will help you learn deep neural networks and their applications in computer vision, generative models, and natural language processing. Found inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Found insideThis open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Found inside – Page iiThis book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. Found insideThis two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September ... Found insideIn this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019. Found insideIt is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly. Found insideThe main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. Found insideGet to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning ... Found inside – Page iWhile highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data ... This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. Found inside... Word2Vec improved version Sub-sampling Word pairs and phrases Negative ... and Implementing GRU GRU with PyTorch Understanding the Sequence to Sequence ... Found insideThis book teaches you to leverage deep learning models in performing various NLP tasks along with showcasing the best practices in dealing with the NLP challenges. In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research. Found insideUsing 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 ... Found insideEach chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Efficient Query Processing for Scalable Web Search will be a valuable reference for researchers and developers working on This tutorial provides an accessible, yet comprehensive, overview of the state-of-the-art of Neural Information ... Found insideThis book is about making machine learning models and their decisions interpretable. What You Will Learn Master tensor operations for dynamic graph-based calculations using PyTorch Create PyTorch transformations and graph computations for neural networks Carry out supervised and unsupervised learning using PyTorch Work with ... Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. Found insideThis book summarizes the organized competitions held during the first NIPS competition track. The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. Found insideThis book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Found inside – Page iThe three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. Your hands dirty and put PyTorch to work right away building a tumor classifier! 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