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Bridge the gap between biology and cutting-edge machine learning techniques with this essential guide. Train and interpret deep learning models and answer fundamental biological questions. Whether your background is in biology, machine learning, or software engineering, this book empowers you with the practical skills needed for your deep learning journey. Through step-by-step recipes, authors Natasha Latysheva and Charles Ravarani teach you to train models for tasks like understanding gene regulatory logic, uncovering insights about protein families, and exploring subcellular localization. Each chapter provides self-contained mini-projects that serve as templates for approaching complex biological problems. By the end of this book, you'll be able to: Approach biological problems with a deep learning perspective Train and interpret deep learning models to explore complex biological data Leverage Python code and iPython notebooks to extend your projects Transform your insights into actionable solutions in medicine, genomics, and biotechnology

By taking you through the development of a real web application from beginning to end, the second edition of this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python. Youll learn how to write and run tests before building each part of your app, and then develop the minimum amount of code required to pass those tests. The result? Clean code that works.In the process, youll learn the basics of Django, Selenium, Git, jQuery, and Mock, along with current web development techniques. If youre ready to take your Python skills to the next level, this bookupdated for Python 3.6clearly demonstrates how TDD encourages simple designs and inspires confidence.Dive into the TDD workflow, including the unit test/code cycle and refactoringUse unit tests for classes and functions, and functional tests for user interactions within the browserLearn when and how to use mock objects, and the pros and cons of isolated vs. integrated testsTest and automate your deployments with a staging serverApply tests to the third-party plugins you integrate into your siteRun tests automatically by using a Continuous Integration environmentUse TDD to build a REST API with a front-end Ajax interface

We've arrived in a new era--GenAI is reshaping industries and decision-making processes across the board. As a result, understanding its potential and pitfalls has become crucial. But in order to stay ahead of the curve, you'll need to develop fresh perspectives on leveraging AI beyond mere technical know-how. Geared toward business leaders and tech professionals alike, this book demystifies the strategic integration of AI into business practices, ensuring you're equipped not just to participate but to lead in this new landscape. This insightful guide by industry leaders Rob Thomas, Paul Zikopoulos, and Kate Soule goes beyond the basics, offering real-life success stories and learned lessons to provide a blueprint for meaningful AI engagement. Whether you're a novice or a seasoned expert, you'll come away with an enhanced understanding of GenAI. Recognize the transformative potential of AI in business and how to harness it Navigate the ethical and operational challenges posed by AI with confidence Understand the dynamic interplay between AI technology and business strategy Implement actionable strategies to integrate AI into your organizational culture Step confidently into the role of an AI value creator, equipped to lead and innovate

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.By working with a single case study throughout this thoroughly revised book, youll learn the entire process of exploratory data analysisfrom collecting data and generating statistics to identifying patterns and testing hypotheses. Youll explore distributions, rules of probability, visualization, and many other tools and concepts.New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.Develop an understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyImport data from most sources with Python, rather than rely on data thats cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

Gaining a Tableau Certified Data Analyst title is key to advertising your skill set in a data-hungry world. It advertises your ability to connect to, analyze, and describe data in an industry-leading data-visualization tool. This study guide provides example questions, terminology, walkthroughs, and best practices to help you learn key topics covered by this certification exam. Christopher Gardner, business intelligence analyst and lead Tableau developer for the University of Michigan, provides guidelines for first-time test-takers and a comprehensive review for Tableau veterans. You'll start by learning how to use Tableau's data Prep Builder and the Tableau Desktop software to connect to, prepare, manipulate, and combine datasets. Then you'll dive into the build interface to develop simple yet compelling data visualizations before moving into more complex topics such as table calculations, level-of-detail expressions, parameters, and predictive analytics tools. This book shows you how to: Read, understand, build, and explain data Manipulate data to prepare it for analysis Combine datasets to expand potential insights Navigate through the Tableau Prep and Tableau Desktop interface to work with your data Set up basic data visualizations and write calculations to perform functions for data enhancement Explore and analyze your data through visualization Publish your work to private or public audiences and properly secure access And much more

In today's data-driven world, organizations must manage and analyze vast amounts of information to deliver the insights that give them a competitive advantage. Many turn to the medallion architecture because it's a proven and well-known design. Yet implementing a robust data pipeline can be difficult, particularly when it comes to using the medallion architecture's bronze, silver, and gold layers--done wrong, it can hamper your ability to make data-driven decisions. This practical guide helps you build a medallion architecture the right way with Azure Databricks and Microsoft Fabric. Drawing on hands-on experience from the field, Piethein Strengholt demystifies common assumptions and complex problems you'll face when embarking on a new data architecture. Architects and engineers of all stripes will find answers to the most typical questions along with insights from real organizations about what's worked, what hasn't, and why. You'll learn: Lakehouse and medallion architecture fundamentals and key concepts Design considerations for Azure Databricks and Microsoft Fabric Scaling considerations, including governance, security, automation, and more How to make informed decisions when designing or implementing new data architectures Proven patterns for success that align with broader organizational objectives

Looking to improve the performance of Cypher queries or learn how to model graphs to support business use cases? A graph database like Neo4j can help. In fact, many enterprises are leveraging Neo4j to power their business-critical applications. This book offers practical and concise recipes on how and when to successfully leverage Neo4j into architectures. Authors Christophe Willemsen and Luanne Misquitta walk you through typical Neo4j implementation strategies from proof of concept to iterative improvements and, finally, to production readiness and beyond. By the end of this book, you should understand how to: Make practical decisions in the proof of concept stage to maximize value Revisit and revise your decisions when transitioning to production Configure and implement observability features for in-production data graphs Integrate graph databases into existing enterprise architectures

Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Pythons implementation. Youll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs.How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations.Get a better grasp of numpy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on a local or remote clusterSolve large problems while using less RAM