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Ralston R: The Forecast

The Forecast

An Inside Look at How Successful Leaders Drive Revenue Growth

Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems. Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data. <ul> <li>Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection</li> <li>Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders</li> <li>Use Python and interactive notebooks for hands-on learning</li> <li>Build problem-solving intuition that generalizes beyond biology</li> </ul> Whether you're exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.

Harness AI to Solve Real-World Biology Problems

Deep Learning for Biology

She built the thing everyone said they wanted. Not one person signed up. Not "a few." Not "slower than expected." Zero. After nine years as a compliance manager for a real estate association, L. V. Ivy had heard the same complaints on repeat - agents blindsided by lapsed licenses, brokers with no clean way to track their teams, dues and deadlines scattered across half a dozen disconnected systems. Nine years of if only something like this existed. So when she lost that job, she built it. AI tools made it possible to ship a full platform alone, fast, and every video she watched told her the same thing: build the whole ecosystem and it will sell itself. She spent four months and her unemployment money finding out what that advice leaves out. This is that story, told in order, without the course-seller's polish. It is not an argument against using AI - the tools are real, and she still uses them daily. It is about the part nobody selling a course wants to mention: the tools were never the piece that was missing. Ten short chapters, each closing with two questions pointed straight back at whatever you're building right now: <ul><li>The dream you're being sold - and who profits from it</li><li>The psychology of easy money</li><li>Why a real problem still isn't a business</li><li>Building in a vacuum, and why nobody tells you no</li><li>What "marketing" actually costs</li><li>The warning signs of an ecosystem nobody asked for</li><li>The order she'd follow if she started over</li><li>Keeping your day job while you test the idea</li><li>When it is actually time to quit</li></ul> Written by someone still in it, while the details were fresh - not from the far side of a success story. If you have an idea, an AI subscription, and a plan to go all in, read this first. It's a couple of hours. It may save you four months.

What I Learned Building a Platform Nobody Used

DON'T QUIT YOUR DAY JOB FOR AI

The MIT Press Essential Knowledge series

What lies underneath digital platforms—their invisible architecture and governance that determine everything visible. Every time you unlock your phone, stream a show, or start your car, you’re entering a world shaped by thousands of engineering decisions you’ll never see. Most platform conversations fixate on their visible canopy—business models, user experience, competitive strategy—the “leaves,” in a sense. Platforms by Amrit Tiwana excavates their roots. The book begins with a simple DNA test that separates authentic from faux platforms, then traces the forces turning tractors, pacemakers, and elevators into platforms. Using the platform trinity, the author explores principles behind why platforms behave so differently, why superior technology doesn’t always win, and why—unlike products that reach a final form—platforms never stop evolving. From there the book explains platforms’ two hidden root systems: The first system, architecture, shows how Lego-like “modular” design, frozen interfaces, and prefab building blocks let thousands of outsiders contribute pieces that snap into colossal systems. The second system, governance, describes where architecture confronts human behavior. Elegant architecture flails with poor governance, so both demand discipline.

Platforms

Applications and Case Studies

Digital Twins in Manufacturing

Funktionsreferenzmodell für ERP-Software

Landslide Science: Geospatial Advances in Monitoring, Modeling, and Management presents a detailed exploration of the latest advancements in landslide research, offering a thorough examination of the latest geospatial technologies and methodologies. The book begins with a comprehensive exploration of landslides, starting with an understanding of their nature, including characteristics, types, and historical perspectives. Factors triggering or influencing landslides are examined, such as rainfall, earthquakes, and topographical influences. Remote sensing techniques for landslide investigation are detailed, covering detection, monitoring, and imagery data processing methods using various sensors and technologies. In addition, spatial landslide assessment and mapping are discussed, including inventory mapping, susceptibility assessment, and hazard assessment utilizing different approaches and uncertainty validation methods. Engineering-based landslide analysis focuses on geotechnical aspects, hydrogeological influences, and methodologies for simulations and slope stability analysis. The book also addresses landslide impacts and risk management strategies, covering vulnerability assessment, risk analysis, environmental consequences, prevention, mitigation strategies, and community engagement.

Geospatial Advances in Monitoring, Modeling, and Management

Landslide Science

Our physical world is grounded in three dimensions. To create technology that can reason about and interact with it, our data must be 3D too. This practical guide offers data scientists, engineers, and researchers a hands-on approach to working with 3D data using Python. From 3D reconstruction to 3D deep learning techniques, you'll learn how to extract valuable insights from massive datasets, including point clouds, voxels, 3D CAD models, meshes, images, and more. &gt; <ul> <li>Understand core concepts and representations of 3D data </li><li>Load, manipulate, analyze, and visualize 3D data using powerful Python libraries </li><li>Apply advanced AI algorithms for 3D pattern recognition (supervised and unsupervised) </li><li>Use 3D reconstruction techniques to generate 3D datasets </li><li>Implement automated 3D modeling and generative AI workflows </li><li>Explore practical applications in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous driving </li><li>Build accurate digital environments that spatial AI solutions can leverage </li></ul> Florent Poux is an esteemed authority in the field of 3D data science who teaches and conducts research for top European universities. He's also head professor at the 3D Geodata Academy and innovation director for French Tech 120 companies.

Building Accurate Digital Environments with 3D Point Cloud Workflows

3D Data Science with Python

Sustainable Computing and Optimization

Unlock the full potential of AI-driven automation with this comprehensive guide to building agile, resilient, and sustainable organizations through real-world applications. In an era defined by rapid technological advancements and unprecedented global challenges, businesses must embrace innovation to remain resilient, agile, and competitive. This book explores the transformative role of AI-driven automation in enabling organizations to improve intelligence. It covers a wide spectrum of topics including the integration of AI in automation, cybersecurity, sustainability, performance measurement, and ethical applications in healthcare. It provides readers with insights into how automation technologies are reshaping industries, improving scalability and flexibility, and driving measurable returns on investment. The volume emphasizes practical applications, such as using AI analytics to assess green equity markets, employing automation in education, and ensuring responsible adoption of intelligent systems. By combining theoretical frameworks with real-world case studies, this book serves as a practical reference for academics, industry leaders, and professionals seeking to leverage intelligent automation to achieve resilience, sustainability, and competitive advantage. Readers will find the volume: <ul><li> Provides a multi-disciplinary perspective on intelligent automation, covering AI, machine learning, cybersecurity, and ROI evaluation;</li> <li> Bridges academia and industry with applied case studies, frameworks, and research insights;</li> <li> Addresses timely topics such as sustainability, ethical AI, and automation in healthcare and education; </li> <li> Highlights future trends and research directions for business resilience in a rapidly changing technological landscape.</li></ul> Audience Researchers, academics, IT professionals, and policymakers in the fields of computer science, engineering, management, and data analytics.

Intelligent Automation and Analytics for Business Resilience

Stop Overthinking and Launch Your Dream Business

Fear Is a Liar

In the rapidly evolving world of data and analytics, professionals face the challenge of navigating complex platforms in order to build more efficient solutions. Microsoft Fabric, hailed as Microsoft's "biggest data product in history after SQL Server," offers powerful capabilities but comes with a steep learning curve. The myriad of choices within Fabric can be overwhelming, with multiple ways to tackle tasks, not all of which are equally efficient. This book serves as a definitive roadmap to understanding Microsoft Fabric--and leveraging it to suit your needs. Authors Nikola Ilic and Ben Weissman demystify the core concepts and components necessary to build, manage, and administer robust data solutions within this game-changing product. <ul> <li>Discover the core Microsoft Fabric components and understand key concepts and techniques for building a robust data platform </li><li>Learn to apply Microsoft Fabric effectively in your day-to-day job </li><li>Understand the concept of a lake-centric architecture </li><li>Gain the skills to implement a scalable and efficient end-to-end analytics solution </li><li>Manage and administer a Fabric tenant
</li></ul>

Designing End-To-End Analytics Solutions

Fundamentals of Microsoft Fabric

Scaling a supply chain and logistics SaaS company past founder-led sales is not a sales problem. It is a system problem.
Most founders try to fix it with better reps, sharper pitches, or more outbound. None of that works until the underlying system works. Ideal customer profile. Messaging that holds in someone else's mouth. A sales process with real exit criteria. Pricing that qualifies buyers. Revenue operations that give you numbers you can trust.
GO TO MARKET LIKE YOU MEAN IT: The Tactical Field Guide for Scaling Supply Chain &amp; Logistics SaaS Revenue is the operating playbook for CEOs, founders, and sales leaders at logistics and supply chain SaaS companies between $3M and $50M ARR who need a motion their team can run without them.
This Industry Edition adapts the core framework for the realities of selling into logistics: skeptical operator buyers, long integration horizons, careful procurement cycles, and champions who must defend you internally against CIOs, CFOs, and the operators who use your product at two in the morning when something breaks.
Inside you will find:

A 30-day plan to install a RevOps backbone
A behavioral ICP framework tuned to logistics buyer archetypes
The Industry Relay method for outbound that actually gets replies in this industry
The Unit of Value Test for pricing logistics SaaS beyond per-seat models
A framework for earning operator trust in AI-accelerated GTM
Nineteen chapters covering ICP, messaging, outbound, pricing, RevOps, hiring, forecasting, and reporting
More than thirty-six companion worksheets and templates

With a foreword by Nick Costides, retired Global President of Information Technology at UPS, this is a working reference for founders who want to stop selling with personality and start selling with a system.
If your sales motion depends on you being on every call, this book is for you.

The Tactical Field Guide for Scaling Supply Chain & Logistics SaaS Revenue

Go to Market Like You Mean It

Modern Bioinformatics: From Molecular Insights to Biomedical Innovation explores the rapidly advancing field of bioinformatics, addressing a critical gap in current literature by integrating quantum biology and quantum biosensors with computational bioinformatics. The book offers a thorough exploration of bioinformatics from foundational concepts to translational applications. It begins with molecular structures of proteins, DNA, and RNA, advancing through nano-molecular visualization and structural domain analysis. Readers gain insights into deep learning techniques like AlphaFold for protein structure prediction, AI applications in genomics, and molecular interactions critical to function. The text further covers computational methods in drug discovery, forensic bioinformatics, and molecular evolution. A unique emphasis is placed on the convergence of nanotechnology and quantum innovations in molecular diagnostics and biosensing, including quantum biosensors and quantum computing applications. The final sections focus on computational strategies for drug target identification, biomarker discovery, and clinical trial analysis, providing a holistic view of modern bioinformatics workflows. This book is an indispensable reference for advanced researchers, graduate students, and professionals in bioinformatics, molecular biology, and biomedical engineering seeking to integrate quantum computing and nanotechnology into their work.

From Molecular Insights to Biomedical Innovation

Modern Bioinformatics

The Forecast

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