Perform time series analysis and forecasting confidently with this Python code bank and reference manual Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms Learn different techniques for evaluating, diagnosing, and optimizing your models Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities Book Description To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples. You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods. Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python. What you will learn Understand what makes time series data different from other data Apply imputation and interpolation strategies to handle missing data Implement an array of models for univariate and multivariate time series Plot interactive time series visualizations using hvPlot Explore state-space models and the unobserved components model (UCM) Detect anomalies using statistical and machine learning methods Forecast complex time series with multiple seasonal patterns Use conformal prediction for constructing prediction intervals for time series Who this book is for This book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is a prerequisite. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you’ll discover: Deep learning from first principles The latest features of Keras 3 A primer on JAX, PyTorch, and TensorFlow Image classification and image segmentation Time series forecasting Large Language models Text classification and machine translation Text and image generation—build your own GPT and diffusion models! Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the Technology In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the Book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's Inside Hands-on, code-first learning Comprehensive, from basics to generative AI Intuitive and easy math explanations Examples in Keras, PyTorch, JAX, and TensorFlow About the Reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the Authors François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Quotes Perfect for anyone interested in learning by doing from one of the industry greats. - Anthony Goldbloom, Founder of Kaggle A sharp, deeply practical guide that teaches you how to think from first principles to build models that actually work. - Santiago Valdarrama, Founder of ml.school The most up-to-date and complete guide to deep learning you’ll find today! - Aran Komatsuzaki, EleutherAI Masterfully conveys the true essence of neural networks. A rare case in recent years of outstanding technical writing. - Salvatore Sanfilippo, Creator of Redis

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Data Without Labels you’ll learn: Fundamental building blocks and concepts of machine learning and unsupervised learning Data cleaning for structured and unstructured data like text and images Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE Association rule algorithms like aPriori, ECLAT, SPADE Unsupervised time series clustering, Gaussian Mixture models, and statistical methods Building neural networks such as GANs and autoencoders Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling Association rule algorithms like aPriori, ECLAT, and SPADE Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask How to interpret the results of unsupervised learning Choosing the right algorithm for your problem Deploying unsupervised learning to production Maintenance and refresh of an ML solution Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You’ll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don’t get bogged down in theory—the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. About the Technology Generative AI, predictive algorithms, fraud detection, and many other analysis tasks rely on cheap and plentiful unlabeled data. Machine learning on data without labels—or unsupervised learning—turns raw text, images, and numbers into insights about your customers, accurate computer vision, and high-quality datasets for training AI models. This book will show you how. About the Book Data Without Labels is a comprehensive guide to unsupervised learning, offering a deep dive into its mathematical foundations, algorithms, and practical applications. It presents practical examples from retail, aviation, and banking using fully annotated Python code. You’ll explore core techniques like clustering and dimensionality reduction along with advanced topics like autoencoders and GANs. As you go, you’ll learn where to apply unsupervised learning in business applications and discover how to develop your own machine learning models end-to-end. What's Inside Master unsupervised learning algorithms Real-world business applications Curate AI training datasets Explore autoencoders and GANs applications About the Reader Intended for data science professionals. Assumes knowledge of Python and basic machine learning. About the Author Vaibhav Verdhan is a seasoned data science professional with extensive experience working on data science projects in a large pharmaceutical company. Quotes An invaluable resource for anyone navigating the complexities of unsupervised learning. A must-have. - Ganna Pogrebna, The Alan Turing Institute Empowers the reader to unlock the hidden potential within their data. - Sonny Shergill, Astra Zeneca A must-have for teams working with unstructured data. Cuts through the fog of theory ili Explains the theory and delivers practical solutions. - Leonardo Gomes da Silva, onGRID Sports Technology The Bible for unsupervised learning! Full of real-world applications, clear explanations, and excellent Python implementations. - Gary Bake, Falconhurst Technologies

Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline. Machine Learning for Tabular Data will teach you how to: Pick the right machine learning approach for your data Apply deep learning to tabular data Deploy tabular machine learning locally and in the cloud Pipelines to automatically train and maintain a model Machine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day. About the Technology Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases and other tabular data sources using gradient boosting, deep learning, and generative AI. About the Book Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable. What's Inside Master XGBoost Apply deep learning to tabular data Deploy models locally and in the cloud Build pipelines to train and maintain models About the Reader For readers experienced with Python and the basics of machine learning. About the Authors Mark Ryan is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, Luca Massaron is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books. Quotes

"Deep Learning and AI Superhero" is an extensive resource for mastering the core concepts and advanced techniques in AI and deep learning using TensorFlow, Keras, and PyTorch. This comprehensive guide walks you through topics from foundational neural network concepts to implementing real-world machine learning solutions. You will gain hands-on experience and theoretical knowledge to elevate your AI development skills. What this Book will help me do Develop a solid foundation in neural networks, their structure, and their training methodologies. Understand and implement deep learning models using TensorFlow and Keras effectively. Gain experience using PyTorch for creating, training, and optimizing advanced machine learning models. Learn advanced applications such as CNNs for computer vision, RNNs for sequential data, and Transformers for natural language processing. Deploy AI models on cloud and edge platforms through practical examples and optimized workflows. Author(s) Cuantum Technologies LLC has established itself as a pioneer in creating educational resources for advanced AI technologies. Their team consists of experts and practitioners in the field, combining years of industry and academic experience. Their books are crafted to ensure readers can practically apply cutting-edge AI techniques with clarity and confidence. Who is it for? This book is ideally suited for software developers, AI enthusiasts, and data scientists who have a basic understanding of programming and machine learning concepts. It's perfect for those seeking to enhance their skills and tackle real-world AI challenges. Whether your goals are professional development, research, or personal learning, you'll find practical and detailed guidance throughout this book.

This book teaches solutions architects how to effectively design and implement AI/ML solutions utilizing Google Cloud services. Through detailed explanations, examples, and hands-on exercises, you will understand essential AI/ML concepts, tools, and best practices while building advanced applications. What this Book will help me do Build robust AI/ML solutions using Google Cloud tools such as TensorFlow, BigQuery, and Vertex AI. Prepare and process data efficiently for machine learning workloads. Establish and apply an MLOps framework for automating ML model lifecycle management. Implement cutting-edge generative AI solutions using best practices. Address common challenges in AI/ML projects with insights from expert solutions. Author(s) Kieran Kavanagh is a seasoned principal architect with nearly twenty years of experience in the tech industry. He has successfully led teams in designing, planning, and governing enterprise cloud strategies, and his wealth of experience is distilled into the practical approaches and insights in this book. Who is it for? This book is ideal for IT professionals aspiring to design AI/ML solutions, particularly in the role of solutions architects. It assumes a basic knowledge of Python and foundational AI/ML concepts but is suitable for both beginners and seasoned practitioners. If you're looking to deepen your understanding of state-of-the-art AI/ML applications on Google Cloud, this resource will guide you.

The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You'll Learn Understand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis

Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology Manage the ML lifecycle with MLflow Ingest data and perform basic preprocessing with Spark Explore feature engineering, and use Spark to extract features Train a model with MLlib and build a pipeline to reproduce it Build a data system to combine the power of Spark with deep learning Get a step-by-step example of working with distributed TensorFlow Use PyTorch to scale machine learning and its internal architecture

In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM® zSystems platform, where your mission-critical transactions, data, and applications are installed, is a key aspect of modernizing business-critical applications while maintaining strict service-level agreements (SLAs) and security requirements. This colocation of data and AI empowers your enterprise to optimally and easily deploy and infuse AI capabilities into your enterprise workloads with the most recent and relevant data available in real time, which enables a more transparent, accurate, and dependable AI experience. This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, such as IBM zSystems Integrated Accelerator for AI, and demonstrates how to leverage certain capabilities and components to enable solutions in business-critical use cases, such as fraud detection and credit risk scoring on the platform. Real-time inferencing with AI models, a capability that is critical to certain industries and use cases such as fraud detection, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16™. This publication also describes and demonstrates the implementation and integration of the two end-to-end solutions (fraud detection and credit risk), from developing and training the AI models to deploying the models in an IBM z/OS® V2R5 environment on IBM z16 hardware, and to integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS®) application. We describe performance optimization recommendations and considerations when leveraging AI technology on the IBM zSystems platform, including optimizations for micro-batching in IBM Watson® Machine Learning for z/OS (WMLz). The benefits that are derived from the solutions also are described in detail, which includes how the open-source AI framework portability of the IBM zSystems platform enables model development and training to be done anywhere, including on IBM zSystems, and the ability to easily integrate to deploy on IBM zSystems for optimal inferencing. You can uncover insights at the transaction level while taking advantage of the speed, depth, and securability of the platform. This publication is intended for technical specialists, site reliability engineers, architects, system programmers, and systems engineers. Technologies that are covered include TensorFlow Serving, WMLz, IBM Cloud Pak® for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM Customer Information Control System (IBM CICS), Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC).

While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company. Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equations—just a fast start for engineers and software developers, complete with hands-on examples. This book helps you: Learn what machine learning and deep learning are and what they can accomplish Understand how popular learning algorithms work and when to apply them Build machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlow Train and score regression models and binary and multiclass classification models Build facial recognition models and object detection models Build language models that respond to natural-language queries and translate text to other languages Use Cognitive Services to infuse AI into the apps that you write

Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Printed in full color throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. About the Technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science. This book shows you how to get started. About the Book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications. What's Inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Printed in full color throughout About the Reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Quotes Chollet is a master of pedagogy and explains complex concepts with minimal fuss, cutting through the math with practical Python code. He is also an experienced ML researcher and his insights on various model architectures or training tips are a joy to read. - Martin Görner, Google Immerse yourself into this exciting introduction to the topic with lots of real-world examples. A must-read for every deep learning practitioner. - Sayak Paul, Carted The modern classic just got better. - Edmon Begoli, Oak Ridge National Laboratory Truly the bible of deep learning. - Yiannis Paraskevopoulos, University of West Attica

In 'Distributed Data Systems with Azure Databricks', you will explore the capabilities of Microsoft Azure Databricks as a platform for building and managing big data pipelines. Learn how to process, transform, and analyze data at scale while developing expertise in training distributed machine learning models and integrating them into enterprise workflows. What this Book will help me do Design and implement Extract, Transform, Load (ETL) pipelines using Azure Databricks. Conduct distributed training of machine learning models using TensorFlow and Horovod. Integrate Azure Databricks with Azure Data Factory for optimized data pipeline orchestration. Utilize Delta Engine for efficient querying and analysis of data within Delta Lake. Employ Databricks Structured Streaming to manage real-time production-grade data flows. Author(s) None Palacio is an experienced data engineer and cloud computing specialist, with extensive knowledge of the Microsoft Azure platform. With years of practical application of Databricks in enterprise settings, Palacio provides clear, actionable insights through relatable examples. They bring a passion for innovative solutions to the field of big data automation. Who is it for? This book is ideal for data engineers, machine learning engineers, and software developers looking to master Azure Databricks for large-scale data processing and analysis. Readers should have basic familiarity with cloud platforms, understanding of data pipelines, and a foundational grasp of Python and machine learning concepts. It is perfect for those wanting to create scalable and manageable data workflows.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands-On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn: · Explains basics to advanced concepts of time series · How to design, develop, train, and validate time-series methodologies · What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results · Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. · Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers

Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on! About the Technology Deep learning handles the toughest search challenges, including imprecise search terms, badly indexed data, and retrieving images with minimal metadata. And with modern tools like DL4J and TensorFlow, you can apply powerful DL techniques without a deep background in data science or natural language processing (NLP). This book will show you how. About the Book Deep Learning for Search teaches you to improve your search results with neural networks. You’ll review how DL relates to search basics like indexing and ranking. Then, you’ll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you’ll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn! What's Inside Accurate and relevant rankings Searching across languages Content-based image search Search with recommendations About the Reader For developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed. About the Author Tommaso Teofili is a software engineer with a passion for open source and machine learning. As a member of the Apache Software Foundation, he contributes to a number of open source projects, ranging from topics like information retrieval (such as Lucene and Solr) to natural language processing and machine translation (including OpenNLP, Joshua, and UIMA). He currently works at Adobe, developing search and indexing infrastructure components, and researching the areas of natural language processing, information retrieval, and deep learning. He has presented search and machine learning talks at conferences including BerlinBuzzwords, International Conference on Computational Science, ApacheCon, EclipseCon, and others. You can find him on Twitter at @tteofili. Quotes A practical approach that shows you the state of the art in using neural networks, AI, and deep learning in the development of search engines. - From the Foreword by Chris Mattmann, NASA JPL A thorough and thoughtful synthesis of traditional search and the latest advancements in deep learning. - Greg Zanotti, Marquette Partners A well-laid-out deep dive into the latest technologies that will take your search engine to the next level. - Andrew Wyllie, Thynk Health Hands-on exercises teach you how to master deep learning for search-based products. - Antonio Magnaghi, System1

Dive deep into how machine learning is transforming the financial industry with 'Machine Learning for Finance'. This comprehensive guide explores cutting-edge concepts in machine learning while providing practical insights and Python code examples to help readers apply these techniques to real-world financial scenarios. Whether tackling fraud detection, financial forecasting, or sentiment analysis, this book equips you with the understanding and tools needed to excel. What this Book will help me do Understand and implement machine learning techniques for structured data, natural language, images, and text. Learn Python-based tools and libraries such as scikit-learn, Keras, and TensorFlow for financial data analysis. Apply machine learning for tasks like predicting financial trends, detecting fraud, and customer sentiment analysis. Explore advanced topics such as neural networks, generative adversarial networks (GANs), and reinforcement learning. Gain hands-on experience with machine learning debugging, products launch preparation, and addressing bias in data. Author(s) James Le None and Jannes Klaas are experts in machine learning applications in financial technology. Jannes has extensive experience training financial professionals on implementing machine learning strategies in their work and pairs this with a deep academic understanding of the topic. Their dedication to empowering readers to confidently integrate AI and machine learning into financial applications shines through in this user-focused, richly detailed book. Who is it for? This book is tailored for financial professionals, data scientists, and enthusiasts aiming to harness machine learning's potential in finance. Readers should have a foundational understanding of mathematics, statistics, and Python programming. If you work in financial services and are curious about applications ranging from fraud detection to trend forecasting, this resource is for you. It's designed for those looking to advance their skills and make impactful contributions in financial technology.

As enterprise-scale data science sharpens its focus on data-driven decision making and machine learning, new tools have emerged to help facilitate these processes. This practical ebook shows data scientists and enterprise developers how the notebook interface, Apache Spark, and other collaboration tools are particularly well suited to bridge the communication gap between their teams. Through a series of real-world examples, author Jerome Nilmeier demonstrates how to generate a model that enables data scientists and developers to share ideas and project code. You’ll learn how data scientists can approach real-world business problems with Spark and how developers can then implement the solution in a production environment. Dive deep into data science technologies, including Spark, TensorFlow, and the Jupyter Notebook Learn how Spark and Python notebooks enable data scientists and developers to work together Explore how the notebook environment works with Spark SQL for structured data Use notebooks and Spark as a launchpad to pursue supervised, unsupervised, and deep learning data models Learn additional Spark functionality, including graph analysis and streaming Explore the use of analytics in the production environment, particularly when creating data pipelines and deploying code

"Hands-On Deep Learning with Apache Spark" is an essential resource for mastering distributed deep learning frameworks and applications on Apache Spark. Through practical examples and guided tutorials, this book teaches you to deploy scalable deep learning solutions for handling complex data challenges efficiently. What this Book will help me do Understand how to set up Apache Spark for deep learning workflows. Gain practical insight into implementing neural networks, including CNNs and RNNs, on distributed platforms. Learn to train and optimize models using popular frameworks like TensorFlow and Keras. Develop expertise in analyzing large datasets with textual and image-based deep learning methods. Acquire skills to deploy trained models for real-world applications in distributed environments. Author(s) None Iozzia is an accomplished software engineer and data scientist with a strong background in distributed computing and machine learning. With years of experience working with Apache Spark and deep learning technologies, None brings a wealth of practical knowledge to the table. Their passion for providing clear, hands-on guidance makes this book an approachable and valuable resource for learners of all levels. Who is it for? This book is aimed at Scala developers, data scientists, and data analysts who are looking to extend their skill set to include distributed deep learning on Apache Spark. It's ideally suited for readers familiar with machine learning basics and those with prior exposure to Apache Spark workflows. If you aim to create scalable machine learning solutions that handle complex data, this book offers precisely what you need.

"Hands-On Artificial Intelligence for Beginners" is your gateway to understanding and implementing modern AI technologies. This book introduces foundational AI concepts, delves into machine learning, deep learning, and neural networks, and guides you through practical applications in real-world scenarios. What this Book will help me do Understand and apply core AI and machine learning principles using tools like TensorFlow. Develop and train artificial neural networks for various applications. Implement advanced models like CNNs, RNNs, and generative models to solve real-world tasks. Explore reinforcement learning techniques and their game-playing strategies. Design, deploy, and optimize scalable AI systems for long-term use. Author(s) None Dindi and Patrick D. Smith are experts in Artificial Intelligence with extensive teaching and development experience. They dedicate their writing to demystifying complex ideas and making them accessible to learners. Their commitment to hands-on practice ensures that readers build concrete skills while grasping theoretical concepts. Who is it for? If you're an aspiring data scientist or developer keen to break into Artificial Intelligence, this book is perfect for you. Beginners with basic programming knowledge will feel comfortable progressing through the material. Readers looking for practical illustrations of AI concepts will benefit greatly from the hands-on approach. This book is tailored for learners aiming to build and deploy real-world AI systems efficiently.