Tokenize
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Tokenize It! Unlocking Value in Real-World Assets
Unlock the potential of the digital revolution with "Tokenize It! Unlocking Value in Real-World Assets," your essential guide to navigating the transformative world of asset tokenization. This comprehensive eBook delves into the powerful intersection of blockchain technology and real-world assets, offering a roadmap to the future of finance and investment. Start your journey with an exploration of the evolution of tokenization and the critical role of blockchain as its backbone. Discover the intricacies of security tokens, their regulatory landscapes, and the myriad ways in which traditional assets can now be reimagined in a digital format. Whether you're interested in real estate, art, or commodities, learn how tokenization is opening new avenues for fractional ownership and liquidity, breaking down investment barriers that once seemed insurmountable. Through a detailed case study on real estate tokenization, gain insights into innovative investment opportunities and the real-world successes that are reshaping markets. Dive deeper into the crucial topics of cybersecurity, risk management, and the integration of decentralized finance (DeFi) with traditional finance. Understand how to evaluate tokenized investments and diversify portfolios, setting a strong foundation for financial growth in an increasingly digital economy. With chapters dedicated to legal frameworks, technological innovations, and tokenization platforms, you'll gain a comprehensive understanding of the critical elements shaping this digital transformation. Explore how tokenization is impacting financial professionals, democratizing access to investments, and addressing social and economic inequalities. As you navigate potential challenges and uncover solutions, prepare to embrace the digital asset revolution. "Tokenize It!" equips you with the knowledge to seize the opportunities of the future, offering insights into the emerging trends and technological advances that will define the new age of finance. Embrace the future of tokenized assets, and position yourself at the forefront of this dynamic and ever-evolving landscape.
Applied Natural Language Processing in the Enterprise
Author: Ankur A. Patel
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-05-12
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production
Learn AI with Python
Build AI applications using Python to intelligently interact with the world around you. KEY FEATURES ● Covers the practical aspects of Machine Learning and Deep Learning concepts with the help of this example-rich guide to Python. ● Includes graphical illustrations of Natural Language Processing and its implementation in NLTK. ● Covers deep learning models such as R-CNN and YOLO for object recognition and teaches how to build an image classifier using CNN. DESCRIPTION The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems. WHAT YOU WILL LEARN ● Learn to implement various machine learning and deep learning algorithms to achieve smart results. ● Understand how ML algorithms can be applied to real-life applications. ● Explore logic programming and learn how to use it practically to solve real-life problems. ● Learn to develop different types of artificial neural networks with Python. ● Understand reinforcement learning and how to build an environment and agents using Python. ● Work with NLTK and build an automatic speech recognition system. WHO THIS BOOK IS FOR This book is for anyone interested in learning about artificial intelligence and putting it into practice with Python. This book is also valuable for intermediate Machine Learning practitioners as a reference guide. Readers should be familiar with the fundamental understanding of Python programming and machine learning techniques. TABLE OF CONTENTS 1. Introduction to AI and Python 2. Machine Learning and Its Algorithms 3. Classification and Regression Using Supervised Learning 4. Clustering Using Unsupervised Learning 5. Solving Problems with Logic Programming 6. Natural Language Processing with Python 7. Implementing Speech Recognition with Python 8. Implementing Artificial Neural Network (ANN) with Python 9. Implementing Reinforcement Learning with Python 10. Implementing Deep Learning and Convolutional Neural Network