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Podcasts
Sebastian Raschka, Vahid Mirjalili
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. 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 working 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, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This 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.
Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data.This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries.This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark.By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data.
Team Foundation Server (TFS) allows you to manage code repositories, build processes, test infrastructure, and deploy labs. TFS supports your team, enabling you to connect, collaborate, and deliver on time. Microsoft's approach to Application Lifecycle Management (ALM) provides a flexible and agile environment that adapts to the needs of your team, removes barriers between roles, and streamlines processes.The book introduces you to creating and setting up team projects for scrum teams. You'll explore various source control repositories, branching, and merging activities, along with a demonstration of how to embed quality into every code check-in. Then, you'll discover agile project planning and management tools. Later, emphasis is given to the testing and release management features of TFS which facilitate the automation of the release pipeline in order to create potentially shippable increments. By the end of the book, you'll have learned to extend and customize TFS plugins to incorporate them into other platforms and enable teams to manage the software lifecycle effectively.
Developers are changing their deployment artifacts from application binaries to container images, giving rise to the need to build container-based apps as part of their new development workflow. Managing an app’s life cycle is complex and requires effort—this book will show you how to efficiently develop, share, and execute applications.You’ll learn how to automate the build and delivery process using CI/CD tools with containers as container orchestrators manage the complexity of running cluster-wide applications, creating infrastructure abstraction layers, while your applications run with high availability, resilience, and persistence. As you advance, you’ll develop, test, and debug applications on your desktop and get them ready to run in production with optimal security standards, using deployment patterns and monitoring tools to help identify common issues. You’ll also review deployment patterns that’ll enable you to solve common deployment problems, providing high availability, scalability, and security to your applications. Finally, you’ll explore different solutions to monitor, log, and instrument your applications as per open-source community standards.By the end of this book, you’ll be able to manage your app’s life cycle by implementing CI/CD workflows using containers to automate the building and delivery of its components.
Large volumes of data are generated daily from applications, websites, IoT devices, and other free-text, semi-structured data sources. Azure Synapse Data Explorer helps you collect, store, and analyze such data, and work with other analytical engines, such as Apache Spark, to develop advanced data science projects and maximize the value you extract from data.This book offers a comprehensive view of Azure Synapse Data Explorer, exploring not only the core scenarios of Data Explorer but also how it integrates within Azure Synapse. From data ingestion to data visualization and advanced analytics, you’ll learn to take an end-to-end approach to maximize the value of unstructured data and drive powerful insights using data science capabilities. With real-world usage scenarios, you’ll discover how to identify key projects where Azure Synapse Data Explorer can help you achieve your business goals. Throughout the chapters, you'll also find out how to manage big data as part of a software as a service (SaaS) platform, as well as tune, secure, and serve data to end users.By the end of this book, you’ll have mastered the big data life cycle and you'll be able to implement advanced analytical scenarios from raw telemetry and log data.
Hands-On Exploratory Data Analysis with R will help you build a strong foundation in data analysis and get well-versed with elementary ways to analyze data. You will learn how to understand your data and summarize its characteristics. You'll also study the structure of your data, and you'll explore graphical and numerical techniques using the R language.This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems.By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, uncover hidden insights, and present your results in a business context.
Unlock the power of AI in your applications with ChatGPT with this practical guide that shows you how to seamlessly integrate OpenAI APIs into your projects, enabling you to navigate complex APIs and ensure seamless functionality with ease.This new edition is updated with key topics such as OpenAI Embeddings, which’ll help you understand the semantic relationships between words and phrases. You’ll find out how to use ChatGPT, Whisper, and DALL-E APIs through 10 AI projects using the latest OpenAI models, GPT-3.5, and GPT-4, with Visual Studio Code as the IDE. Within these projects, you’ll integrate ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. You’ll get to grips with NLP tasks, build a ChatGPT clone, and create an AI code bug-fixing SaaS app. The chapters will also take you through speech recognition, text-to-speech capabilities, language translation, generating email replies, creating PowerPoint presentations, and fine-tuning ChatGPT, along with adding payment methods by integrating the ChatGPT API with Stripe.By the end of this book, you’ll be able to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.
Prasenjit Sarkar, Guillermo Ruiz
Oracle Cloud Infrastructure (OCI) is a set of complementary cloud services that enables you to build and run a wide range of applications and services in a highly available hosted environment. This book is a fast-paced practical guide that will help you develop the capabilities to leverage OCI services and effectively manage your cloud infrastructure.Oracle Cloud Infrastructure for Solutions Architects begins by helping you get to grips with the fundamentals of Oracle Cloud Infrastructure, and moves on to cover the building blocks of the layers of Infrastructure as a Service (IaaS), such as Identity and Access Management (IAM), compute, storage, network, and database. As you advance, you’ll delve into the development aspects of OCI, where you’ll learn to build cloud-native applications and perform operations on OCI resources as well as use the CLI, API, and SDK. Finally, you’ll explore the capabilities of building an Oracle hybrid cloud infrastructure.By the end of this book, you’ll have learned how to leverage the OCI and gained a solid understanding of the persona of an architect as well as a developer’s perspective.